library(readxl) # for reading in excel files
library(janitor) # data checks and cleaning
library(glue) # for easy pasting
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(rstatix) # for stats
library(pheatmap) # for heatmaps
library(plotly) # for interactive plots
library(htmlwidgets) # for saving interactive plots
library(devtools)
library(notame) # used for feature clustering
library(doParallel)
library(igraph) # feature clustering
library(ggpubr) # visualizations
library(knitr) # clean table printing
library(rmarkdown)
library(corrr)
library(ggcorrplot)
library(ggthemes)
library(ggtext)
library(PCAtools)
library(pathview) # for functional analysis and KEGG annotation
library(mixOmics) #multilevel PCA and sPLS-DA
library(tidyverse) # for everything## [1] 1043
## # A tibble: 4 × 89
## mz_rt dupe_count row_id x5101_b1_control_c18…¹ x5105_b3_lyc_c18neg_21
## <chr> <int> <dbl> <dbl> <dbl>
## 1 724.5276_8.3 2 2843 4697. 8713.
## 2 724.5276_8.3 2 2846 4697. 8713.
## 3 863.6765_10.6… 2 3900 17536. 12167.
## 4 863.6765_10.6… 2 3903 17536. 12167.
## # ℹ abbreviated name: ¹x5101_b1_control_c18neg_82
## # ℹ 84 more variables: x5112_b1_beta_c18neg_34 <dbl>,
## # x5107_b1_beta_c18neg_29 <dbl>, x5105_b1_lyc_c18neg_76 <dbl>,
## # x5102_b1_control_c18neg_5 <dbl>, x5101_b3_control_c18neg_66 <dbl>,
## # x5103_b1_lyc_c18neg_35 <dbl>, x5104_b3_lyc_c18neg_32 <dbl>,
## # x5109_b3_beta_c18neg_81 <dbl>, x5104_b1_lyc_c18neg_10 <dbl>,
## # x5108_b1_control_c18neg_71 <dbl>, x5113_b3_beta_c18neg_47 <dbl>, …
# remove dupes
omicsdata <- omicsdata %>%
distinct(mz_rt, .keep_all = TRUE)
# check again for dupes
omicsdata %>% get_dupes(mz_rt)## # A tibble: 0 × 89
## # ℹ 89 variables: mz_rt <chr>, dupe_count <int>, row_id <dbl>,
## # x5101_b1_control_c18neg_82 <dbl>, x5105_b3_lyc_c18neg_21 <dbl>,
## # x5112_b1_beta_c18neg_34 <dbl>, x5107_b1_beta_c18neg_29 <dbl>,
## # x5105_b1_lyc_c18neg_76 <dbl>, x5102_b1_control_c18neg_5 <dbl>,
## # x5101_b3_control_c18neg_66 <dbl>, x5103_b1_lyc_c18neg_35 <dbl>,
## # x5104_b3_lyc_c18neg_32 <dbl>, x5109_b3_beta_c18neg_81 <dbl>,
## # x5104_b1_lyc_c18neg_10 <dbl>, x5108_b1_control_c18neg_71 <dbl>, …
## [1] 1041
Sometimes a weird logical column (lgl) comes up in my data. Let’s check if it’s there
## [1] "mz_rt" "row_id"
## [3] "x5101_b1_control_c18neg_82" "x5105_b3_lyc_c18neg_21"
## [5] "x5112_b1_beta_c18neg_34" "x5107_b1_beta_c18neg_29"
## [7] "x5105_b1_lyc_c18neg_76" "x5102_b1_control_c18neg_5"
## [9] "x5101_b3_control_c18neg_66" "x5103_b1_lyc_c18neg_35"
## [11] "x5104_b3_lyc_c18neg_32" "x5109_b3_beta_c18neg_81"
## [13] "x5104_b1_lyc_c18neg_10" "x5108_b1_control_c18neg_71"
## [15] "x5113_b3_beta_c18neg_47" "x5111_b1_control_c18neg_4"
## [17] "x5109_b1_beta_c18neg_65" "x5111_b3_control_c18neg_39"
## [19] "x5110_b1_lyc_c18neg_59" "x5102_b3_control_c18neg_33"
## [21] "x5108_b3_control_c18neg_22" "x5103_b3_lyc_c18neg_63"
## [23] "x5107_b3_beta_c18neg_28" "x5112_b3_beta_c18neg_56"
## [25] "x5113_b1_beta_c18neg_53" "x5110_b3_lyc_c18neg_84"
## [27] "x5114_b1_beta_c18neg_26" "x5114_b3_beta_c18neg_23"
## [29] "x5122_b3_lyc_c18neg_75" "x5124_b3_control_c18neg_68"
## [31] "x5117_b3_lyc_c18neg_2" "x5120_b1_beta_c18neg_72"
## [33] "x5115_b1_lyc_c18neg_36" "x5125_b3_control_c18neg_51"
## [35] "x5116_b3_control_c18neg_20" "x5126_b3_beta_c18neg_24"
## [37] "x5118_b3_lyc_c18neg_9" "x5117_b1_lyc_c18neg_46"
## [39] "x5123_b3_control_c18neg_40" "x5115_b3_lyc_c18neg_70"
## [41] "x5122_b1_lyc_c18neg_14" "x5125_b1_control_c18neg_80"
## [43] "x5123_b1_control_c18neg_11" "x5116_b1_control_c18neg_6"
## [45] "x5120_b3_beta_c18neg_54" "x5121_b1_beta_c18neg_69"
## [47] "x5121_b3_beta_c18neg_48" "x5118_b1_lyc_c18neg_57"
## [49] "x5124_b1_control_c18neg_45" "x5119_b3_control_c18neg_58"
## [51] "x5119_b1_control_c18neg_74" "x5127_b1_control_c18neg_30"
## [53] "x5131_b1_beta_c18neg_38" "x5135_b1_lyc_c18neg_12"
## [55] "x5129_b3_lyc_c18neg_16" "x5131_b3_beta_c18neg_3"
## [57] "x5134_b3_beta_c18neg_64" "x5133_b3_control_c18neg_15"
## [59] "x5127_b3_control_c18neg_83" "x5126_b1_beta_c18neg_62"
## [61] "x5136_b1_beta_c18neg_44" "x5133_b1_control_c18neg_27"
## [63] "x5134_b1_beta_c18neg_42" "x5135_b3_lyc_c18neg_18"
## [65] "x5128_b1_lyc_c18neg_8" "x5128_b3_lyc_c18neg_60"
## [67] "x5136_b3_beta_c18neg_77" "x5132_b1_beta_c18neg_41"
## [69] "x5132_b3_beta_c18neg_52" "x5129_b1_lyc_c18neg_78"
## [71] "x5130_b3_lyc_c18neg_50" "x5130_b1_lyc_c18neg_17"
## [73] "qc2_c18neg_7" "qc1_c18neg_1"
## [75] "qc3_c18neg_13" "qc4_c18neg_19"
## [77] "qc5_c18neg_25" "qc7_c18neg_37"
## [79] "qc8_c18neg_43" "qc6_c18neg_31"
## [81] "qc14_c18neg_79" "qc13_c18neg_73"
## [83] "qc12_c18neg_67" "qc10_c18neg_55"
## [85] "qc15_c18neg_85" "qc11_c18neg_61"
## [87] "qc9_c18neg_49" "x88"
# remove lgl column
omicsdata <- omicsdata %>%
dplyr::select(!where(is.logical))
colnames(omicsdata)## [1] "mz_rt" "row_id"
## [3] "x5101_b1_control_c18neg_82" "x5105_b3_lyc_c18neg_21"
## [5] "x5112_b1_beta_c18neg_34" "x5107_b1_beta_c18neg_29"
## [7] "x5105_b1_lyc_c18neg_76" "x5102_b1_control_c18neg_5"
## [9] "x5101_b3_control_c18neg_66" "x5103_b1_lyc_c18neg_35"
## [11] "x5104_b3_lyc_c18neg_32" "x5109_b3_beta_c18neg_81"
## [13] "x5104_b1_lyc_c18neg_10" "x5108_b1_control_c18neg_71"
## [15] "x5113_b3_beta_c18neg_47" "x5111_b1_control_c18neg_4"
## [17] "x5109_b1_beta_c18neg_65" "x5111_b3_control_c18neg_39"
## [19] "x5110_b1_lyc_c18neg_59" "x5102_b3_control_c18neg_33"
## [21] "x5108_b3_control_c18neg_22" "x5103_b3_lyc_c18neg_63"
## [23] "x5107_b3_beta_c18neg_28" "x5112_b3_beta_c18neg_56"
## [25] "x5113_b1_beta_c18neg_53" "x5110_b3_lyc_c18neg_84"
## [27] "x5114_b1_beta_c18neg_26" "x5114_b3_beta_c18neg_23"
## [29] "x5122_b3_lyc_c18neg_75" "x5124_b3_control_c18neg_68"
## [31] "x5117_b3_lyc_c18neg_2" "x5120_b1_beta_c18neg_72"
## [33] "x5115_b1_lyc_c18neg_36" "x5125_b3_control_c18neg_51"
## [35] "x5116_b3_control_c18neg_20" "x5126_b3_beta_c18neg_24"
## [37] "x5118_b3_lyc_c18neg_9" "x5117_b1_lyc_c18neg_46"
## [39] "x5123_b3_control_c18neg_40" "x5115_b3_lyc_c18neg_70"
## [41] "x5122_b1_lyc_c18neg_14" "x5125_b1_control_c18neg_80"
## [43] "x5123_b1_control_c18neg_11" "x5116_b1_control_c18neg_6"
## [45] "x5120_b3_beta_c18neg_54" "x5121_b1_beta_c18neg_69"
## [47] "x5121_b3_beta_c18neg_48" "x5118_b1_lyc_c18neg_57"
## [49] "x5124_b1_control_c18neg_45" "x5119_b3_control_c18neg_58"
## [51] "x5119_b1_control_c18neg_74" "x5127_b1_control_c18neg_30"
## [53] "x5131_b1_beta_c18neg_38" "x5135_b1_lyc_c18neg_12"
## [55] "x5129_b3_lyc_c18neg_16" "x5131_b3_beta_c18neg_3"
## [57] "x5134_b3_beta_c18neg_64" "x5133_b3_control_c18neg_15"
## [59] "x5127_b3_control_c18neg_83" "x5126_b1_beta_c18neg_62"
## [61] "x5136_b1_beta_c18neg_44" "x5133_b1_control_c18neg_27"
## [63] "x5134_b1_beta_c18neg_42" "x5135_b3_lyc_c18neg_18"
## [65] "x5128_b1_lyc_c18neg_8" "x5128_b3_lyc_c18neg_60"
## [67] "x5136_b3_beta_c18neg_77" "x5132_b1_beta_c18neg_41"
## [69] "x5132_b3_beta_c18neg_52" "x5129_b1_lyc_c18neg_78"
## [71] "x5130_b3_lyc_c18neg_50" "x5130_b1_lyc_c18neg_17"
## [73] "qc2_c18neg_7" "qc1_c18neg_1"
## [75] "qc3_c18neg_13" "qc4_c18neg_19"
## [77] "qc5_c18neg_25" "qc7_c18neg_37"
## [79] "qc8_c18neg_43" "qc6_c18neg_31"
## [81] "qc14_c18neg_79" "qc13_c18neg_73"
## [83] "qc12_c18neg_67" "qc10_c18neg_55"
## [85] "qc15_c18neg_85" "qc11_c18neg_61"
## [87] "qc9_c18neg_49"
# create long df for omics df
omicsdata_tidy <- omicsdata %>%
pivot_longer(cols = 3:ncol(.),
names_to = "sample",
values_to = "peak_height") %>%
mutate(sample2 = sample) %>%
# add a new column with just subject
rename("subject" = sample2) %>%
# remove the suffix from subject names
mutate_at("subject", str_sub, start=2, end=5)## tibble [88,485 × 18] (S3: tbl_df/tbl/data.frame)
## $ subject : chr [1:88485] "5101" "5105" "5112" "5107" ...
## $ sample : chr [1:88485] "x5101_b1_control_c18neg_82" "x5105_b3_lyc_c18neg_21" "x5112_b1_beta_c18neg_34" "x5107_b1_beta_c18neg_29" ...
## $ treatment : chr [1:88485] "control" "red" "beta" "beta" ...
## $ tomato_or_control: chr [1:88485] "control" "tomato" "tomato" "tomato" ...
## $ mz : num [1:88485] 273 273 273 273 273 ...
## $ rt : num [1:88485] 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 ...
## $ row_id : num [1:88485] 62 62 62 62 62 62 62 62 62 62 ...
## $ peak_height : num [1:88485] 11987 15548 14778 13083 14824 ...
## $ sex : chr [1:88485] "F" "M" "F" "M" ...
## $ bmi : num [1:88485] 29.4 33.5 26 23.8 33.5 ...
## $ age : num [1:88485] 55 61 60 68 61 62 55 50 37 65 ...
## $ tot_chol : num [1:88485] 235 189 235 177 189 229 235 172 203 127 ...
## $ ldl_chol : num [1:88485] 150.4 125.6 149.2 85.4 125.6 ...
## $ hdl_chol : num [1:88485] 71 45 52 79 45 40 71 53 31 40 ...
## $ triglycerides : num [1:88485] 68 92 169 63 92 184 68 38 125 88 ...
## $ glucose : num [1:88485] 90 103 92 102 103 94 90 92 93 87 ...
## $ SBP : num [1:88485] 156 131 107 122 131 101 156 106 100 147 ...
## $ DBP : num [1:88485] 90 78 70 58 78 62 90 71 67 76 ...
# replace NA's in certain columns with QC
meta_omics_sep$subject <- str_replace_all(meta_omics_sep$subject, "c", "qc")
meta_omics_sep$sample <- meta_omics_sep$sample %>%
replace_na("QC")
meta_omics_sep$treatment <- meta_omics_sep$treatment %>%
replace_na("QC")
meta_omics_sep$tomato_or_control <- meta_omics_sep$tomato_or_control %>%
replace_na("QC")# plot
(plot_mzvsrt <- meta_omics_sep %>%
ggplot(aes(x = rt, y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "Retention time, min",
y = "m/z",
title = "mz across RT for all features"))# samples only (no QCs)
omicsdata_noQC <- omicsdata %>%
dplyr::select(-contains("qc"))
#NAs in samples only?
NAbyRow_noQC <- rowSums(is.na(omicsdata_noQC[,-1]))
hist(NAbyRow_noQC,
breaks = 70, # because there are 70 samples
xlab = "Number of missing values",
ylab = "Number of metabolites",
main = "How many missing values are there?")Are there any missing values in QCs? There shouldn’t be after data preprocessing/filtering
omicsdata_QC <- omicsdata %>%
dplyr::select(starts_with("qc"))
NAbyRow_QC <- colSums(is.na(omicsdata_QC))
# lets confirm that there are no missing values from my QCs
sum(NAbyRow_QC) # no## [1] 0
# calculate how many NAs there are per feature in whole data set
contains_NAs <- meta_omics %>%
group_by(mz_rt) %>%
count(is.na(peak_height)) %>%
filter(`is.na(peak_height)` == TRUE)
kable(contains_NAs)| mz_rt | is.na(peak_height) | n |
|---|---|---|
| 1315.8467_3.079 | TRUE | 52 |
| 137.0244_0.741 | TRUE | 6 |
| 151.0261_0.612 | TRUE | 15 |
| 1602.9935_2.726 | TRUE | 2 |
| 1604.0878_6.358 | TRUE | 3 |
| 1605.0902_6.358 | TRUE | 1 |
| 1668.2129_8.463 | TRUE | 1 |
| 1669.2155_8.46 | TRUE | 1 |
| 187.0515_0.642 | TRUE | 7 |
| 191.1076_0.694 | TRUE | 66 |
| 201.0226_0.688 | TRUE | 12 |
| 229.0537_0.703 | TRUE | 5 |
| 230.9967_0.644 | TRUE | 6 |
| 231.0795_0.662 | TRUE | 32 |
| 235.0972_0.69 | TRUE | 63 |
| 241.0868_1.048 | TRUE | 61 |
| 255.0871_0.663 | TRUE | 1 |
| 257.0795_0.669 | TRUE | 52 |
| 326.0874_0.626 | TRUE | 63 |
| 352.0856_0.687 | TRUE | 2 |
| 391.2849_2.246 | TRUE | 5 |
| 397.1499_0.653 | TRUE | 64 |
| 411.1294_0.644 | TRUE | 66 |
| 421.1543_0.701 | TRUE | 68 |
| 437.0542_0.688 | TRUE | 15 |
| 437.2904_2.11 | TRUE | 7 |
| 448.3062_1.477 | TRUE | 1 |
| 449.254_2.573 | TRUE | 1 |
| 453.285_1.371 | TRUE | 47 |
| 462.1763_0.655 | TRUE | 5 |
| 464.3013_0.999 | TRUE | 18 |
| 473.3474_4.696 | TRUE | 47 |
| 507.223_2.273 | TRUE | 2 |
| 514.2836_0.69 | TRUE | 31 |
| 517.2436_2.68 | TRUE | 60 |
| 559.4722_4.208 | TRUE | 1 |
| 624.3379_0.689 | TRUE | 1 |
| 624.3382_0.91 | TRUE | 19 |
| 784.549_6.62 | TRUE | 27 |
| 800.5587_8.868 | TRUE | 1 |
| 818.5544_4.825 | TRUE | 38 |
| 830.5005_2.723 | TRUE | 6 |
| 881.5332_7.243 | TRUE | 4 |
There are some features that are missing in a majority of subjects. I’m going to remove those because they may skew the data.
## [1] 1041
# Removing features missing from over 90% of data
omit_features <- contains_NAs %>%
filter(n/70 >= 0.90)
#preview
nrow(omit_features) # features to remove## [1] 6
## [1] 1035
# now remove these features from the omics dataset
omicsdata <- omicsdata %>%
anti_join(omit_features,
by = "mz_rt")
# check number of features now?
nrow(omicsdata)## [1] 1035
# impute any missing values by replacing them with 1/2 of the lowest peak height value of a feature (i.e. in a row).
imputed_omicsdata <- omicsdata
imputed_omicsdata[] <- lapply(imputed_omicsdata,
function(x) ifelse(is.na(x),
min(x, na.rm = TRUE)/2, x))
dim(imputed_omicsdata)## [1] 1035 87
Are there any NAs?
## [1] 0
# create long df for imputed omics df
imputed_omicsdata_tidy <- imputed_omicsdata %>%
pivot_longer(cols = 3:ncol(.),
names_to = "sample",
values_to = "peak_height") %>%
mutate(sample2 = sample) %>%
# add a new column with just subject
rename("subject" = sample2) %>%
# remove the suffix from subject names
mutate_at("subject", str_sub, start=2, end=5)
# combine meta and imputed omics dfs
imp_meta_omics <- full_join(imputed_omicsdata_tidy,
metadata,
by = c("subject" = "subject"))vignette for reference
Let’s look at what masses come up at each RT again
# rt vs mz plot
imp_meta_omics_sep %>%
ggplot(aes(x = rt, y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "RT (min)",
y = "mz")
There are some points that are at the same RT, meaning they could be
coming from the same compound. We’ll run notame clustering to collapse
features coming from one mass into one feature.
# create features list from imputed data set to only include unique feature ID's (mz_rt), mz and RT
features <- imp_meta_omics_sep %>%
cbind(imp_meta_omics$mz_rt) %>%
rename("mz_rt" = "imp_meta_omics$mz_rt") %>%
dplyr::select(c(mz_rt, mz, rt)) %>%
distinct() # remove the duplicate rows
# create a second data frame which is just imp_meta_omics restructured to another wide format
data_notame <- data.frame(imputed_omicsdata %>%
dplyr::select(-row_id) %>%
t())
data_notame <- data_notame %>%
tibble::rownames_to_column() %>% # change samples from rownames to its own column
row_to_names(row_number = 1) # change the feature IDs (mz_rt) from first row obs into column namesCheck structures
## 'data.frame': 1035 obs. of 3 variables:
## $ mz_rt: chr "272.9587_0.485" "288.9363_0.485" "226.9658_0.499" "294.9532_0.5" ...
## $ mz : num 273 289 227 295 363 ...
## $ rt : num 0.485 0.485 0.499 0.5 0.501 0.514 0.514 0.515 0.515 0.515 ...
## mz_rt 272.9587_0.485 288.9363_0.485 226.9658_0.499
## 2 x5101_b1_control_c18neg_82 11986.5490 21534.8980 28760.8710
## 294.9532_0.5 362.9406_0.501 520.9099_0.514 588.8973_0.514 452.9225_0.515
## 2 22338.5640 34403.1300 114667.8360 98069.3900 102553.1200
## 656.8848_0.515 724.8722_0.515 792.8596_0.515 384.9351_0.516 316.9477_0.518
## 2 59991.9960 46757.6500 28809.4430 97462.6900 129814.3400
## 604.8712_0.518 248.9604_0.52 215.0328_0.588 217.0297_0.589 151.0261_0.612
## 2 43247.6520 139909.1600 423488.8000 147959.5300 1008.0625
## 335.0471_0.599 649.1192_0.601 167.021_0.602 665.0885_0.607 643.1067_0.611
## 2 89597.6100 17832.4840 169550.3600 17469.2250 21769.1270
## 627.1374_0.611 329.0355_0.613 621.1249_0.613 459.1113_0.613 935.1964_0.613
## 2 35917.1130 44170.4260 19929.6100 12582.7990 6787.0806
## 919.2277_0.616 605.1555_0.616 313.0652_0.617 291.0839_0.618 128.0352_0.617
## 2 9664.9620 76629.0500 232746.4500 1622794.8000 35370.3630
## 583.1735_0.618 247.0933_0.62 293.0879_0.621 191.0197_0.621 111.0086_0.62
## 2 16841.3160 30654.0470 35340.3240 643951.4000 150325.2200
## 950.1575_0.623 636.085_0.627 658.0669_0.628 680.0486_0.63 389.052_0.635
## 2 3818.7810 17712.3800 17415.8850 6436.0660 23057.9500
## 263.1035_0.638 277.0734_0.639 343.9948_0.639 172.9913_0.642 187.0418_0.642
## 2 66218.5000 41315.5550 216509.7300 115296.5800 209861.0800
## 187.0515_0.642 345.9989_0.642 300.0047_0.643 201.0575_0.644 188.9862_0.644
## 2 14098.1120 6601.0000 23712.5230 48690.5860 9552.8840
## 89.0243_0.645 230.9967_0.644 93.0344_0.646 178.0509_0.647 479.0972_0.649
## 2 239368.3800 1436.5206 31256.6620 23041.9240 31571.9530
## 103.04_0.651 462.1763_0.655 187.007_0.66 203.0013_0.66 107.0502_0.661
## 2 48823.0800 3000.7876 600991.2000 7830.9595 110424.4450
## 194.0457_0.66 255.0871_0.663 383.1529_0.667 187.0165_0.665 117.0557_0.669
## 2 3220.5076 8777.2920 1042.2191 16411.9530 56197.1560
## 291.0953_0.67 245.0486_0.685 352.0856_0.687 201.0226_0.688 413.1998_0.688
## 2 15675.7380 17020.8540 8563.7650 1560.3474 6060.3726
## 437.0542_0.688 510.2523_0.688 624.3379_0.689 514.2836_0.69 213.0223_0.689
## 2 1315.9800 6418.2686 6967.8380 504.0312 3920.3600
## 397.1781_0.69 129.0557_0.69 367.1581_0.691 257.0795_0.669 528.2631_0.691
## 2 13469.2600 126977.6400 36765.0980 504.0312 7024.1000
## 231.0795_0.662 397.2049_0.693 229.0537_0.703 244.908_0.706 246.9051_0.707
## 2 1033.3367 10682.8890 1605.1570 28453.5800 26781.3090
## 369.1735_0.722 137.0244_0.741 624.3382_0.91 239.0923_0.975 464.3013_0.999
## 2 16647.0840 2228.7122 2404.1950 13168.3100 504.0312
## 241.0868_1.048 448.3063_1.326 453.285_1.371 583.2555_1.358 448.3062_1.477
## 2 504.0312 2249.9277 504.0312 8213.1480 2152.3337
## 267.1235_1.539 507.223_1.7 437.2904_2.11 586.3143_2.221 391.2849_2.246
## 2 14312.3720 28249.8280 2109.4116 7916.0166 7834.5107
## 562.3143_2.27 507.223_2.273 297.1527_2.281 512.2988_2.28 311.2224_2.465
## 2 6131.5140 5463.4160 4852.7866 18104.6400 4982.1772
## 538.3144_2.488 449.254_2.573 612.33_2.594 640.2923_2.673 656.3173_2.673
## 2 26836.0620 3670.2560 27236.7710 8631.6280 11211.9810
## 578.3011_2.673 588.3302_2.677 526.3144_2.683 311.1683_2.682 1107.6619_2.7
## 2 11934.0640 156921.7200 14725.9940 10671.6130 6479.0815
## 517.2436_2.68 524.2776_2.71 830.5005_2.723 1151.649_2.727 504.3089_2.726
## 2 504.0312 17717.5160 1502.1267 2805.0450 18348.5370
## 564.3308_2.727 1135.6242_2.73 556.299_2.728 694.2877_2.728 1083.6621_2.728
## 2 396449.9700 2101.6200 10870.7270 11570.6170 15289.0240
## 616.2924_2.728 554.3012_2.728 1073.6331_2.728 632.3174_2.729 1602.9935_2.726
## 2 20911.8340 28204.3280 3184.2246 25367.3900 1053.2416
## 619.2886_2.757 500.2777_2.802 614.3455_2.824 476.2777_2.847 590.3457_2.989
## 2 12485.9420 35047.2400 7444.0780 27902.5570 39904.7900
## 1521.9681_3.075 670.2878_3.073 1165.6197_3.074 532.2989_3.075 530.3013_3.075
## 2 2808.4710 21550.0350 6353.8237 23154.2200 62886.2770
## 1025.6334_3.075 592.2925_3.075 540.3313_3.076 608.3174_3.076 1035.6627_3.076
## 2 12987.2740 42669.5270 1157627.5000 52637.2970 93593.6800
## 654.31_3.076 1103.6493_3.076 480.309_3.076 1087.6246_3.076 1531.9972_3.076
## 2 8150.1400 14690.8430 47245.8500 10563.3290 7595.6387
## 1520.9647_3.076 1027.6336_3.076 1530.994_3.076 1598.9806_3.076
## 2 3523.7397 6002.6210 10113.0330 2481.0215
## 1315.8467_3.079 820.5155_3.079 1061.6776_3.089 452.2777_3.197 618.308_3.232
## 2 504.0312 2857.1274 7589.4805 23911.5370 14584.3490
## 634.3329_3.232 506.3245_3.232 566.3462_3.232 556.3168_3.233 696.3033_3.233
## 2 20147.4020 10277.2140 273539.3400 25799.9820 7031.4870
## 1087.6933_3.233 558.3148_3.234 478.2933_3.354 524.3351_3.376 526.3505_3.4
## 2 7558.6090 8811.2780 16215.1490 21914.5800 18850.2320
## 592.3612_3.407 554.3456_3.441 436.2827_3.522 552.3663_3.532 698.3189_3.775
## 2 7309.2260 20630.9320 13906.4640 11245.3920 8278.1290
## 1159.7117_3.776 1081.6957_3.777 508.3402_3.777 568.3621_3.777 620.3237_3.777
## 2 4318.5205 5479.5150 16299.1320 532299.4400 24095.6450
## 1143.6869_3.778 636.3486_3.777 558.3325_3.778 1091.7248_3.778 626.3198_3.778
## 2 3467.0476 39472.6130 42671.3800 21538.7910 10507.0020
## 560.3303_3.778 1615.0859_3.778 594.3768_3.858 480.309_3.883 599.3194_3.921
## 2 14168.7450 2119.9607 6830.7280 45650.4400 15658.0970
## 583.2554_3.939 605.2373_3.94 1167.518_3.94 554.3818_4.037 414.0583_4.043
## 2 23206.5250 6507.5527 5111.5010 7755.4146 8064.4450
## 253.2169_4.044 327.2325_4.055 511.3996_4.087 464.314_4.127 303.2326_4.149
## 2 3715.8516 40238.2850 3120.4510 17825.3830 43368.2270
## 440.0739_4.207 442.071_4.208 279.2327_4.208 444.0682_4.208 559.4722_4.208
## 2 56946.7030 46998.2070 129523.0160 17429.4550 8758.2900
## 463.3422_4.219 537.4153_4.24 539.4304_4.266 539.4306_4.266 573.4515_4.266
## 2 4484.4497 33903.0470 13237.3580 13237.3580 11812.4580
## 445.3316_4.28 467.3735_4.306 591.462_4.314 445.3316_4.415 539.4309_4.416
## 2 16675.6540 24954.8000 5188.5474 22877.8380 10927.5510
## 511.3997_4.453 447.3471_4.499 493.389_4.514 447.3471_4.631 473.3629_4.632
## 2 3652.2422 11653.1750 5953.3700 11625.6970 5888.9800
## 567.4621_4.643 446.0838_4.646 444.0866_4.647 281.2484_4.648 442.0896_4.647
## 2 4465.4155 19138.6820 50135.1560 200246.7800 51583.4450
## 563.5036_4.648 591.462_4.651 449.3627_4.663 593.4778_4.664 575.467_4.662
## 2 23532.0410 6583.1655 9165.3930 35374.8830 3355.5427
## 473.3474_4.696 425.363_4.712 441.3942_4.718 491.3733_4.784 818.5544_4.825
## 2 504.0312 12562.7590 4651.2950 4547.7983 504.0312
## 551.3582_4.841 573.4515_4.873 561.3787_4.908 577.3738_4.918 617.4753_4.956
## 2 1659.1428 5616.5020 8191.6600 5274.4893 3398.3782
## 595.4933_4.953 691.5022_4.977 495.4046_4.993 397.3681_5 717.5179_5.106
## 2 23644.1680 11188.6350 4821.5300 6125.1910 27211.0740
## 465.3192_5.112 465.3038_5.114 591.3894_5.174 705.5178_5.308 479.3193_5.338
## 2 8263.3955 110951.5100 11246.6020 6977.3240 6625.6714
## 575.4672_5.354 411.3837_5.349 605.4051_5.422 579.3893_5.557 878.584_5.681
## 2 3909.9460 7838.0220 6459.4814 2674.2950 4946.8696
## 1394.0685_5.684 787.5209_5.683 709.5047_5.684 719.534_5.684 771.4961_5.685
## 2 4091.1677 22534.1900 23499.2420 222421.5600 10974.9320
## 711.5035_5.685 849.4911_5.685 493.3349_5.699 722.4967_5.728 904.599_5.836
## 2 9168.9520 6182.0674 10544.2640 3234.9639 6464.1490
## 1447.1028_5.846 1446.0998_5.845 875.5066_5.845 735.5203_5.846 813.5365_5.846
## 2 8393.4350 10199.0020 9896.5600 37915.4380 33982.0820
## 737.5193_5.846 745.5498_5.846 859.5292_5.846 803.5076_5.847 797.5117_5.847
## 2 15724.5500 381718.6600 6394.4097 11439.6400 18180.0400
## 1271.7465_5.862 848.5434_5.871 822.5279_5.875 748.5122_5.882 824.5433_5.925
## 2 9264.0960 5360.7744 6334.9043 3378.6714 5833.0910
## 818.554_5.972 818.5539_5.971 721.5493_6 721.549_6 848.5433_6.017
## 2 6304.4487 9040.8240 8293.6070 8293.6070 5426.5117
## 798.5279_6.032 733.5494_6.091 801.5366_6.09 723.5203_6.092 765.5735_6.098
## 2 26310.0040 126621.0100 13265.0940 15401.5330 6608.7260
## 771.5646_6.096 764.4992_6.1 842.5154_6.099 774.5282_6.1 898.559_6.11
## 2 12778.7190 5535.0215 6602.7666 55365.6680 3685.2770
## 763.5596_6.181 1068.6675_6.19 801.547_6.254 790.5149_6.254 800.5438_6.256
## 2 13917.5860 13256.2270 26554.4240 8090.8223 54200.4000
## 868.531_6.255 874.559_6.259 759.5646_6.287 836.5434_6.295 557.4566_6.309
## 2 7316.0400 9442.5750 9749.2750 8236.6190 8280.5880
## 1605.0902_6.358 1604.0878_6.358 882.5021_6.358 824.5441_6.359 814.5148_6.359
## 2 2503.2397 1919.8804 6036.3770 157509.4700 18936.0020
## 892.531_6.359 816.5151_6.359 876.5059_6.36 954.5018_6.361 840.5304_6.407
## 2 16966.9240 7578.4750 9698.0330 5114.8510 8459.3210
## 850.5595_6.407 790.5148_6.407 918.5466_6.408 800.5433_6.41 868.5308_6.41
## 2 65896.5100 6673.6377 8493.9850 57832.4060 6448.3480
## 801.547_6.409 812.5436_6.468 884.518_6.476 879.5263_6.489 816.5318_6.478
## 2 29100.6300 21515.1350 4892.3020 5314.6626 13349.5430
## 818.5302_6.479 956.5168_6.479 894.5465_6.48 1608.119_6.481 826.5598_6.482
## 2 5292.8687 4111.5107 12525.5670 1456.6576 111413.3000
## 878.5241_6.495 1530.1292_6.506 1529.1254_6.507 896.5849_6.505 906.6148_6.513
## 2 15045.6350 7696.4670 7073.6133 5749.3050 25188.6130
## 1519.1193_6.539 1491.1091_6.544 747.5662_6.538 1450.1318_6.54 1441.1057_6.541
## 2 10325.6550 3698.7026 1126503.9000 65371.4060 11406.0780
## 1518.1184_6.541 1502.0921_6.54 739.5351_6.541 1440.1025_6.541 861.5449_6.542
## 2 12439.5910 7361.1310 34892.3630 13591.2950 12531.6820
## 799.5274_6.542 831.526_6.542 764.5548_6.543 737.5362_6.543 815.5524_6.543
## 2 40890.1100 7634.1377 12854.4380 91910.2660 83607.6500
## 877.5224_6.543 1503.101_6.546 788.5437_6.549 805.5234_6.55 883.5394_6.551
## 2 23042.7340 8019.2725 27396.0900 19062.6580 13138.2480
## 945.5095_6.552 1451.1351_6.539 801.5445_6.55 800.5402_6.543 750.528_6.591
## 2 7267.6800 57017.0430 18246.8000 35417.1300 27264.1720
## 820.5694_6.598 784.549_6.62 1151.7045_6.635 838.5592_6.651 876.5748_6.674
## 2 5591.0806 504.0312 16743.6840 3385.6418 5824.5205
## 814.5592_6.699 773.5806_6.749 763.5516_6.749 841.5661_6.75 1579.1408_6.75
## 2 5335.2430 156829.1900 17065.5530 17132.0640 7385.7656
## 1656.1198_6.753 850.5603_6.753 1582.1048_6.758 842.5312_6.754 1657.1231_6.754
## 2 8507.5730 383956.2200 3297.0112 17908.6230 8909.0860
## 853.572_6.759 1646.0906_6.756 840.5306_6.756 902.5217_6.756 867.5493_6.755
## 2 16663.1880 2485.1140 41751.7730 22894.2030 6170.3340
## 986.5339_6.756 918.5467_6.756 964.5391_6.756 970.4999_6.757 908.5177_6.758
## 2 8439.8430 40072.3000 5975.6445 5105.9140 13004.3440
## 980.5167_6.758 776.5438_6.762 1647.0986_6.756 844.5327_6.766 766.5148_6.768
## 2 11173.4750 87543.6100 2520.8220 13258.0540 8478.3030
## 828.5061_6.771 808.5482_6.817 546.1884_6.859 550.1831_6.859 545.3463_6.86
## 2 5257.7920 2870.9343 30991.6600 10394.8545 24574.5300
## 519.347_6.861 548.1856_6.861 557.457_6.865 559.4719_6.867 543.3083_6.873
## 2 18584.6290 31491.5490 3774.5525 4240.5786 8250.8710
## 560.2269_6.891 561.2272_6.889 876.5748_6.891 473.3439_6.896 739.5515_6.896
## 2 5078.9230 5696.8022 8479.1440 10678.1790 11276.6630
## 645.4854_6.897 749.5806_6.906 749.5804_6.907 816.5308_6.915 818.5304_6.915
## 2 6815.6978 23554.2560 23554.2560 92555.0160 35193.7660
## 946.5031_6.923 843.5493_6.926 884.518_6.929 1600.0951_6.94 962.534_6.937
## 2 8240.4930 11779.9900 15864.8000 7945.2354 9319.6450
## 878.522_6.938 1599.0952_6.94 1598.0913_6.94 894.5469_6.945 1676.1068_6.945
## 2 34961.7900 9671.2290 9272.7360 70189.6300 8662.0300
## 956.5171_6.945 940.5392_6.946 1677.1104_6.945 1608.1204_6.95 1609.1238_6.95
## 2 21859.5800 10560.0190 7693.5790 37296.6400 38017.9400
## 826.5606_6.953 886.5195_6.961 1660.08_6.945 810.5642_6.968 1575.0947_6.977
## 2 828296.4400 6658.6170 4503.8657 2061.9120 6851.5527
## 1652.1073_6.976 1574.0913_6.977 1584.1204_6.981 1585.1238_6.981
## 2 7466.5205 8431.1530 34260.9100 33486.1950
## 795.5319_7.041 1561.1246_7.026 802.5612_7.026 1560.1212_7.025 871.5502_7.054
## 2 14637.8060 87123.4700 1357793.5000 96113.1700 41827.1520
## 1551.0949_7.03 1628.1079_7.03 1550.0916_7.031 1629.1111_7.028 917.5422_7.049
## 2 14490.1470 14638.6000 13662.6840 12221.6460 8263.7550
## 1612.0825_7.034 792.5308_7.046 794.5298_7.047 916.5393_7.053 1610.1346_7.034
## 2 7057.5340 86525.6500 35337.5980 12739.0300 22415.3320
## 761.5803_7.053 932.5173_7.054 1611.1384_7.055 854.522_7.057 870.547_7.057
## 2 19171.0760 23813.2150 9165.0530 34324.8630 84686.4800
## 1520.0675_7.067 886.5199_7.016 762.507_7.071 860.5179_7.078 938.534_7.079
## 2 3811.9190 8245.6875 32796.7460 15533.7880 10826.0980
## 922.5024_7.08 1000.5039_7.081 852.5752_7.104 844.5463_7.11 920.5621_7.108
## 2 7704.4320 7322.0195 120860.0800 6777.9770 14412.5760
## 842.546_7.109 904.5364_7.114 1586.1348_7.163 878.5901_7.193 720.4962_7.178
## 2 14882.3710 7048.3574 3374.4092 4193.5596 5568.1133
## 830.5815_7.188 1613.1542_7.188 942.5549_7.193 896.5624_7.191 958.5324_7.191
## 2 39760.6330 4131.9590 6944.3384 34076.4450 10640.0040
## 818.5462_7.192 1612.1504_7.187 880.5375_7.198 828.5759_7.19 820.5463_7.196
## 2 34030.3320 4982.6816 16646.3570 269664.9700 14294.4050
## 886.5328_7.202 948.5172_7.192 964.5495_7.201 881.5397_7.202 1602.121_7.207
## 2 11507.0640 4908.6304 8291.8480 9636.1840 1752.1300
## 788.5223_7.225 834.5643_7.242 881.5332_7.243 807.5001_7.254 790.5593_7.252
## 2 3845.7795 16588.3650 7665.8945 5144.8730 25775.3700
## 864.5747_7.263 738.507_7.269 806.4942_7.271 746.5118_7.267 878.5903_7.305
## 2 7477.0130 39627.3120 5332.4136 3596.2605 7596.1035
## 840.5749_7.321 1602.1228_7.339 948.5173_7.337 886.5331_7.335 897.5657_7.337
## 2 16394.4220 3432.8390 8086.6675 16539.9980 28673.6520
## 1026.5195_7.338 964.5495_7.339 818.5462_7.337 958.5325_7.337 942.5548_7.339
## 2 7702.3306 12165.5910 55049.5550 15019.3560 9148.1640
## 1603.1254_7.34 880.5376_7.34 828.5759_7.339 896.5624_7.34 820.5462_7.34
## 2 4002.3570 25622.8140 430864.8000 51577.6800 22564.8650
## 1612.1508_7.34 845.5636_7.343 1637.1538_7.351 1613.1545_7.34 912.5357_7.346
## 2 10090.1740 8650.3250 3598.2053 10747.8955 5677.1580
## 714.507_7.348 1636.1506_7.352 920.5621_7.373 854.5867_7.421 881.5405_7.337
## 2 21358.9750 3177.6514 9580.4890 18861.1040 12995.1875
## 852.5752_7.369 842.546_7.377 904.5361_7.374 843.5493_7.379 836.5799_7.404
## 2 69645.9300 9225.8790 5982.6323 5791.6274 12651.9100
## 764.5225_7.43 857.5176_7.442 800.5354_7.468 810.5647_7.467 878.5516_7.469
## 2 12744.6630 32854.1400 17461.8550 132522.1700 17583.5500
## 840.5749_7.471 862.527_7.471 868.5303_7.476 862.5261_7.47 800.5357_7.469
## 2 30003.9410 10110.1800 6806.3706 10110.1800 17461.8550
## 922.5767_7.503 854.5896_7.505 740.5225_7.515 833.5175_7.518 855.5937_7.505
## 2 5766.1430 29425.2870 7085.3794 24162.9400 17361.9450
## 862.5953_7.53 884.5607_7.548 776.5354_7.574 816.5751_7.553 806.5463_7.553
## 2 7405.0166 8423.6980 11808.3970 44148.6520 6009.2563
## 833.5544_7.557 884.5587_7.554 843.5839_7.561 767.5664_7.562 905.5552_7.563
## 2 10528.5640 7110.3060 32967.9600 12791.6700 10971.1730
## 765.5672_7.563 775.5965_7.563 786.5646_7.575 854.5519_7.576 776.5351_7.576
## 2 32845.1330 235038.4500 73149.2340 8554.0030 11808.3970
## 778.5595_7.588 768.5305_7.588 830.5218_7.589 846.5477_7.59 836.5799_7.605
## 2 106386.6200 13095.3370 6905.6960 15131.2390 24562.9730
## 883.5333_7.614 746.5121_7.622 814.4993_7.622 812.5802_7.632 864.5398_7.662
## 2 6894.4070 42279.5470 5209.3210 90708.6200 7775.5156
## 802.5529_7.634 880.5669_7.637 1179.7355_7.664 859.5339_7.699 855.5938_7.701
## 2 15428.4590 14540.6350 6533.5780 4189.1255 24039.5940
## 764.5224_7.695 922.5774_7.695 854.5906_7.701 788.5795_7.719 856.5627_7.726
## 2 5594.5650 7022.3516 41821.7930 29939.3220 5997.8154
## 828.5728_7.674 906.5814_7.742 838.5956_7.75 1598.1714_7.758 864.535_7.755
## 2 18499.0640 7983.9310 54082.3300 5964.2130 9644.0780
## 883.5331_7.753 862.5335_7.761 940.5496_7.764 1616.1136_7.768 924.518_7.766
## 2 6612.6390 20426.8340 13661.2270 4574.5190 10243.3730
## 888.5362_7.768 821.565_7.768 1633.1422_7.775 872.5626_7.769 1002.5196_7.769
## 2 7455.2085 11493.6920 8331.0010 87200.1640 6726.3164
## 856.5377_7.769 918.5543_7.768 796.5458_7.771 1555.1258_7.773 934.5326_7.772
## 2 38901.9020 13405.0330 32951.0600 8986.0300 24030.7560
## 794.5464_7.772 1554.1226_7.773 804.5762_7.774 918.5546_7.77 857.5409_7.768
## 2 84274.0600 9048.8280 816784.5600 13405.0330 20997.4380
## 1564.1517_7.775 1632.139_7.776 1565.1551_7.774 772.5276_7.782 1639.1701_7.792
## 2 35523.3500 7901.5444 31104.6350 13494.5000 7250.4443
## 1638.1667_7.792 878.5909_7.805 936.5439_7.803 1008.5435_7.813 870.5618_7.814
## 2 6213.1940 86858.2300 5752.7476 4836.8890 5132.1055
## 946.5777_7.814 868.5616_7.814 930.5504_7.815 1482.0881_7.834 801.6117_7.833
## 2 11800.3270 11311.2530 5586.2490 4683.7026 72313.0800
## 869.598_7.834 791.5828_7.838 824.5789_7.841 814.5949_7.87 864.6108_7.878
## 2 10249.8300 12324.8370 3535.8100 13830.4150 3409.7360
## 722.5123_7.861 790.4993_7.863 820.4801_7.866 780.4705_7.867 814.595_7.869
## 2 115125.7100 12781.5880 6431.2144 6948.0825 13830.4150
## 859.5338_7.864 814.5947_7.872 842.5905_7.891 827.6272_7.905 812.58_7.898
## 2 5668.9233 13830.4150 7701.9053 22540.6450 14386.5610
## 789.6114_7.911 792.5765_7.938 698.5121_7.962 777.6118_7.986 1558.1192_8.016
## 2 5187.0140 5050.9270 17849.7710 14208.7510 3159.3452
## 1052.5353_8.011 990.5651_8.013 974.5321_8.013 912.5491_8.014 748.5277_8.014
## 2 8414.3040 12534.1690 7662.0980 20708.5880 46733.6900
## 906.5531_8.016 922.5781_8.016 871.5805_8.017 846.5617_8.018 1655.1569_8.02
## 2 29787.6840 73024.1950 10395.6310 28928.2580 5415.9004
## 984.5483_8.018 1654.1535_8.02 938.5513_8.018 968.5707_8.019 844.562_8.02
## 2 18987.9920 5260.1740 5926.1855 11094.8690 72785.1500
## 1665.186_8.021 854.5918_8.021 1664.1825_8.021 914.5508_8.03 724.5275_8.059
## 2 15773.8000 597630.8000 16215.7400 9704.4000 6238.5977
## 1631.1566_8.057 814.5957_8.055 1630.1533_8.057 1641.1859_8.06 1640.1826_8.061
## 2 4667.3135 6033.9720 3949.3613 11569.0040 13329.3710
## 1616.183_8.109 945.5734_8.118 1685.1732_8.11 830.5921_8.11 1617.1865_8.109
## 2 30387.2130 9049.9750 8840.7930 810380.7500 33352.4530
## 1684.1698_8.112 1607.1572_8.113 1606.1538_8.113 880.6062_8.116
## 2 8998.0205 9455.1980 8503.0580 24042.2500
## 1668.1442_8.113 944.5705_8.116 822.5616_8.116 820.562_8.117 960.5481_8.117
## 2 5463.8247 13576.0260 37789.9770 85715.2500 24214.5020
## 847.5799_8.118 898.5782_8.119 853.6428_8.12 882.5533_8.121 883.5565_8.118
## 2 12919.2750 89561.4300 24748.5620 37676.0900 19931.1780
## 716.5225_8.139 888.549_8.143 914.5514_8.091 950.5333_8.144 890.5493_8.144
## 2 7779.8290 23152.9470 9903.8900 9753.9375 10276.3010
## 1028.5349_8.145 724.5277_8.141 966.5651_8.147 724.5275_8.142 790.5382_8.16
## 2 8666.2690 7835.7803 18252.3700 7835.7803 21106.9320
## 789.6116_8.177 840.6105_8.214 762.5643_8.206 856.6051_8.21 582.5094_8.227
## 2 11812.9610 4544.3486 18487.7930 13209.3460 10990.9070
## 572.4806_8.229 574.4787_8.23 838.5955_8.279 748.5276_8.299 724.5276_8.3
## 2 21580.7810 7488.7440 8260.9810 8657.7880 4696.7030
## 835.5322_8.336 764.5798_8.343 818.5906_8.346 862.5953_8.355 909.5492_8.36
## 2 16368.2705 15757.4500 17764.0410 6502.1250 8638.5590
## 902.5128_8.383 774.5431_8.358 824.4968_8.383 834.5254_8.385 766.5385_8.385
## 2 4093.0002 2007.0992 8713.0370 16660.0000 102631.6000
## 864.5064_8.385 788.58_8.386 815.627_8.417 736.5275_8.432 1669.2155_8.46
## 2 8448.3670 22432.6460 9359.7300 4345.7607 1621.7545
## 1668.2129_8.463 840.6112_8.445 810.5275_8.448 841.6406_8.452 976.5477_8.457
## 2 2019.3225 9788.1990 33076.2660 11286.6170 3996.8179
## 914.5643_8.457 848.577_8.458 846.5774_8.459 986.5637_8.459 908.5688_8.459
## 2 9376.7980 11834.7740 28534.2230 8202.1890 13272.8750
## 992.5806_8.459 924.5935_8.459 970.5858_8.459 856.6068_8.46 873.5954_8.46
## 2 6013.5340 27763.8200 5290.1294 152973.2700 5111.0654
## 742.5383_8.471 792.5536_8.473 1697.1555_8.491 885.5495_8.509 790.5953_8.511
## 2 33108.3300 5459.1440 2762.2256 219215.0300 23723.5000
## 953.536_8.516 1015.5064_8.516 880.6062_8.517 983.5161_8.518 1021.5212_8.523
## 2 26731.9820 7852.9300 12420.5690 12558.5250 8244.5660
## 943.5073_8.524 1005.4968_8.528 864.6108_8.544 828.5668_8.612 838.5956_8.612
## 2 11931.8100 10613.6810 4830.8320 5983.6357 25094.9750
## 906.5828_8.614 929.5358_8.639 861.5489_8.641 911.5645_8.686 793.5985_8.748
## 2 5728.3066 6024.2460 37498.0820 4402.3364 19151.1910
## 803.6275_8.749 750.5432_8.694 871.6147_8.752 844.6062_8.707 814.5957_8.732
## 2 103716.1600 12871.6200 20451.3440 5184.2810 13738.5530
## 806.5905_8.734 806.5895_8.732 804.6308_8.768 855.59_8.755 861.5846_8.743
## 2 18060.4510 17002.6970 49662.9960 12279.3850 10165.6400
## 795.5979_8.768 793.5985_8.765 842.5306_8.773 774.5433_8.775 803.6275_8.771
## 2 9425.6810 21094.1760 5530.6550 35749.2460 103716.1600
## 871.6147_8.768 933.5855_8.781 840.6113_8.8 830.5838_8.802 908.5977_8.803
## 2 20451.3440 6936.8920 35263.4140 7860.8755 8485.7410
## 700.5277_8.805 819.6141_8.816 829.6431_8.816 821.6152_8.817 856.6029_8.818
## 2 8064.3228 12680.5370 59890.4530 5772.9780 8446.2010
## 887.599_8.816 768.554_8.827 804.6307_8.782 882.6209_8.855 872.5955_8.863
## 2 5530.6300 6555.8037 49662.9960 14926.7080 3023.7730
## 950.6082_8.859 866.6268_8.879 800.5587_8.868 816.6106_8.908 968.581_8.937
## 2 3764.7932 8463.0010 2290.4570 9929.1650 6303.9160
## 946.5851_8.939 824.5771_8.938 962.5631_8.939 890.5649_8.939 900.5936_8.939
## 2 4725.8300 11668.0690 7632.1910 10249.5800 28638.6050
## 832.6067_8.94 884.5687_8.94 822.5774_8.941 897.6305_9.024 829.6432_9.024
## 2 144460.3400 13710.0490 27029.8960 28433.5620 172523.2500
## 881.6055_9.023 819.6141_9.024 959.6018_9.026 1561.2028_9.038 821.6139_9.026
## 2 13610.3370 29945.2170 9545.7750 3594.8525 13116.5080
## 887.5991_9.03 847.6304_9.032 903.5878_9.033 855.6591_9.033 969.6384_9.033
## 2 9205.0660 16887.5350 6131.2227 210398.3100 6098.1714
## 985.616_9.034 991.6331_9.034 907.6211_9.035 923.646_9.035 913.6162_9.035
## 2 9650.0480 8398.3180 20509.0880 40109.9450 13624.2880
## 845.6298_9.037 750.5436_9.042 818.5306_9.045 848.5114_9.046 808.5018_9.051
## 2 40144.5350 111689.3300 14247.4610 7089.5356 6832.3794
## 888.5678_9.068 887.5646_9.068 776.5589_9.132 726.5434_9.157 794.5306_9.158
## 2 14691.8460 23708.0700 13893.1800 22353.0000 4055.4963
## 858.622_9.163 858.6217_9.164 752.5589_9.222 805.6426_9.222 831.6576_9.28
## 2 11971.4860 12272.2260 5368.9220 5129.8857 4040.8533
## 744.5538_9.321 833.6294_9.395 843.6586_9.396 911.6461_9.397 875.6844_9.397
## 2 7145.4883 9015.5790 34686.9000 7792.0415 6487.9380
## 788.5437_9.413 817.643_9.423 885.6305_9.426 807.6141_9.427 887.5646_9.463
## 2 18918.1290 35230.9020 8296.1730 8985.0670 3790.0000
## 600.5119_9.48 752.5591_9.514 857.6737_9.513 931.5514_9.582 863.5644_9.582
## 2 6794.8594 3570.4470 4379.6440 2323.0913 10587.6030
## 911.6459_9.666 901.6169_9.665 895.6211_9.665 833.6297_9.665 843.6587_9.666
## 2 13474.3500 6688.2840 9071.0300 16480.4040 60810.8400
## 911.646_9.667 1016.7241_9.858 983.6184_9.976 778.5744_9.917 889.617_9.974
## 2 13474.3500 4702.8150 5785.7030 7963.8535 13294.1490
## 945.6385_9.975 967.6322_9.976 1670.349_9.987 883.6211_9.976 821.6298_9.976
## 2 5831.8623 8264.8240 4279.3145 18760.4040 40503.6700
## 961.6178_9.977 899.6459_9.978 823.6294_9.979 831.659_9.98 1644.3337_9.984
## 2 11646.7640 41054.9000 16734.7930 224299.7700 6377.4263
## 993.6488_9.986 857.6749_9.986 971.6542_9.988 915.6319_9.987 987.6318_9.987
## 2 10976.1170 269686.7000 7963.2510 17955.4840 12177.7780
## 925.6617_9.988 847.6455_9.988 849.6462_9.988 917.6329_9.989 874.6643_9.99
## 2 48529.5470 49363.0980 23903.6820 7906.3354 7891.7890
## 905.6028_9.99 909.6368_9.99 931.6162_9.992 983.6185_9.993 728.5589_10.045
## 2 8745.6600 23330.1390 5534.3560 5785.7030 5464.5327
## 931.6162_9.999 653.491_10.071 1261.8136_10.084 1235.798_10.088 894.658_10.086
## 2 5534.3560 5309.9985 6648.7560 6083.2900 6683.0166
## 860.6369_10.19 778.5745_10.284 993.6487_10.29 925.6615_10.291 915.6327_10.293
## 2 4347.8887 16747.3610 9180.6270 42885.4570 17120.4940
## 917.633_10.292 857.6746_10.294 849.6458_10.294 967.594_10.294 931.6164_10.288
## 2 8375.1450 227623.8600 29497.3280 8381.0640 5103.2144
## 909.6366_10.295 847.6455_10.298 905.6039_10.298 874.6642_10.298
## 2 31145.9860 68079.2300 10383.8000 12174.1120
## 920.6735_10.31 871.6891_10.337 939.6769_10.335 923.6524_10.353
## 2 11407.9310 23431.1900 4883.7890 4365.2290
## 1261.8135_10.352 754.5745_10.357 833.6742_10.379 860.6919_10.384
## 2 6244.3438 5013.8345 22248.7150 13928.9730
## 823.6447_10.383 818.6271_10.414 828.6559_10.414 820.627_10.414
## 2 10651.2940 49472.2970 36236.8240 24093.9380
## 854.6714_10.418 1646.3475_10.439 904.6327_10.443 913.6613_10.438
## 2 23159.9770 3366.3650 10757.5170 28688.9120
## 1637.3241_10.442 845.6746_10.439 1636.3208_10.44 903.6327_10.442
## 2 4831.9707 248822.1400 6311.0030 18021.2300
## 897.637_10.442 905.6301_10.443 837.6454_10.443 835.6457_10.444 862.664_10.445
## 2 38940.5000 8713.7110 52024.6130 129008.8500 25511.1270
## 955.5948_10.447 920.6223_10.449 893.604_10.45 895.6025_10.452 816.5752_10.467
## 2 9650.6110 7492.4640 18636.5270 13528.3820 5412.6943
## 1249.8137_10.468 638.5721_10.489 628.5437_10.491 630.5417_10.49
## 2 8322.1470 11466.0350 21496.6580 7993.3060
## 1279.8241_10.52 1018.7397_10.524 1008.7111_10.525 654.5589_10.54
## 2 8390.5910 5776.7700 7643.0054 26564.1640
## 656.5586_10.54 664.5877_10.541 682.5753_10.545 680.5744_10.545 707.593_10.547
## 2 10790.8170 15001.5730 15388.2230 33780.4060 8583.8650
## 690.603_10.546 936.6574_10.551 832.6428_10.551 842.6715_10.551
## 2 16967.4600 4246.1646 43308.2800 19995.1740
## 946.6887_10.548 837.6571_10.552 864.6677_10.558 1263.8295_10.575
## 2 3736.5776 5951.1045 12317.8480 17800.0550
## 885.7055_10.58 862.6535_10.583 872.6815_10.583 911.652_10.584 1675.384_10.589
## 2 5392.2827 8905.0270 6678.5770 34630.8320 6900.4870
## 969.6092_10.587 1674.3803_10.59 1666.3553_10.589 859.6907_10.59
## 2 6806.5720 5760.7236 9989.0205 372254.8400
## 849.6616_10.589 1665.3553_10.589 927.677_10.59 876.68_10.59 851.6615_10.59
## 2 232448.1600 12303.3290 27190.5470 45172.9530 98275.5900
## 917.6482_10.589 1664.3521_10.59 919.6483_10.59 907.6204_10.599 909.6189_10.6
## 2 18074.4650 10063.3940 8632.1950 22921.6370 15571.6400
## 934.6384_10.6 912.6573_10.608 652.5879_10.611 642.5594_10.614 806.6057_10.613
## 2 9350.6690 26177.1800 7656.0300 9989.4400 4466.7417
## 922.6891_10.613 896.6737_10.623 914.6592_10.621 970.6153_10.616
## 2 13885.9320 7693.4927 8838.5730 5592.2676
## 886.645_10.627 856.6871_10.641 874.6819_10.641 846.6588_10.643
## 2 6484.7970 41352.6370 9696.1890 105766.6250
## 908.6381_10.644 695.5929_10.644 678.6034_10.645 904.6171_10.646
## 2 11053.4410 6063.0550 12335.3310 8930.5090
## 670.5743_10.646 668.5747_10.647 853.619_10.659 865.6758_10.678
## 2 7433.1140 18218.1840 7036.0166 7932.8877
## 863.6765_10.683 851.6659_10.671 658.5736_10.694 656.575_10.694
## 2 17536.2340 16954.3140 50116.9900 136334.9000
## 666.6032_10.695 744.5811_10.699 684.591_10.697 682.5904_10.698
## 2 80462.1950 5573.4940 47335.4700 121164.5300
## 692.6191_10.698 709.609_10.707 640.5796_10.729 676.6243_10.731
## 2 66447.8000 23455.6050 2462.6406 3118.6467
## 696.6064_10.749 706.6345_10.754 658.5898_10.758 660.5885_10.757
## 2 11036.6760 7231.1826 23411.7600 8107.9395
## 668.6174_10.756 855.6346_10.759 698.6108_10.776 697.6094_10.776
## 2 15633.9460 8517.0140 12544.2450 27215.1540
## 670.5906_10.779 680.6191_10.777 672.5896_10.777 674.6039_10.834
## 2 125584.6700 62872.6900 44991.6500 5083.6850
## 682.6332_10.835 1335.245_10.854 694.635_10.854 684.6065_10.853
## 2 9162.7830 4942.8203 245587.0300 354283.5000
## 686.6054_10.854 746.5966_10.853 1334.2417_10.855 762.6216_10.854
## 2 148661.9800 11560.1420 5303.9520 4838.7534
## 711.6251_10.854 652.6239_10.859 678.6402_10.885 668.611_10.888
## 2 61655.1900 3495.9722 5296.9190 3014.2336
## 698.6217_10.912 700.6211_10.914 708.6502_10.915 686.6207_10.914
## 2 34974.1330 15591.5400 20487.3600 31390.2580
## 697.653_10.915 688.6203_10.915 696.6498_10.915 725.6412_10.916
## 2 11392.3330 12834.5340 21661.5210 8151.1580
## 722.6659_11.002 680.6552_11.016 960.7412_11.038 986.7566_11.049
## 2 5985.1500 3538.8257 9043.6290 11070.3710
## 988.7722_11.162 1014.7875_11.17
## 2 11237.9790 6091.9590
# change to results to numeric
data_notame <- data_notame %>%
mutate_at(-1, as.numeric)
head(data_notame, n = 1)## mz_rt 272.9587_0.485 288.9363_0.485 226.9658_0.499
## 2 x5101_b1_control_c18neg_82 11986.55 21534.9 28760.87
## 294.9532_0.5 362.9406_0.501 520.9099_0.514 588.8973_0.514 452.9225_0.515
## 2 22338.56 34403.13 114667.8 98069.39 102553.1
## 656.8848_0.515 724.8722_0.515 792.8596_0.515 384.9351_0.516 316.9477_0.518
## 2 59992 46757.65 28809.44 97462.69 129814.3
## 604.8712_0.518 248.9604_0.52 215.0328_0.588 217.0297_0.589 151.0261_0.612
## 2 43247.65 139909.2 423488.8 147959.5 1008.062
## 335.0471_0.599 649.1192_0.601 167.021_0.602 665.0885_0.607 643.1067_0.611
## 2 89597.61 17832.48 169550.4 17469.22 21769.13
## 627.1374_0.611 329.0355_0.613 621.1249_0.613 459.1113_0.613 935.1964_0.613
## 2 35917.11 44170.43 19929.61 12582.8 6787.081
## 919.2277_0.616 605.1555_0.616 313.0652_0.617 291.0839_0.618 128.0352_0.617
## 2 9664.962 76629.05 232746.5 1622795 35370.36
## 583.1735_0.618 247.0933_0.62 293.0879_0.621 191.0197_0.621 111.0086_0.62
## 2 16841.32 30654.05 35340.32 643951.4 150325.2
## 950.1575_0.623 636.085_0.627 658.0669_0.628 680.0486_0.63 389.052_0.635
## 2 3818.781 17712.38 17415.88 6436.066 23057.95
## 263.1035_0.638 277.0734_0.639 343.9948_0.639 172.9913_0.642 187.0418_0.642
## 2 66218.5 41315.56 216509.7 115296.6 209861.1
## 187.0515_0.642 345.9989_0.642 300.0047_0.643 201.0575_0.644 188.9862_0.644
## 2 14098.11 6601 23712.52 48690.59 9552.884
## 89.0243_0.645 230.9967_0.644 93.0344_0.646 178.0509_0.647 479.0972_0.649
## 2 239368.4 1436.521 31256.66 23041.92 31571.95
## 103.04_0.651 462.1763_0.655 187.007_0.66 203.0013_0.66 107.0502_0.661
## 2 48823.08 3000.788 600991.2 7830.959 110424.4
## 194.0457_0.66 255.0871_0.663 383.1529_0.667 187.0165_0.665 117.0557_0.669
## 2 3220.508 8777.292 1042.219 16411.95 56197.16
## 291.0953_0.67 245.0486_0.685 352.0856_0.687 201.0226_0.688 413.1998_0.688
## 2 15675.74 17020.85 8563.765 1560.347 6060.373
## 437.0542_0.688 510.2523_0.688 624.3379_0.689 514.2836_0.69 213.0223_0.689
## 2 1315.98 6418.269 6967.838 504.0312 3920.36
## 397.1781_0.69 129.0557_0.69 367.1581_0.691 257.0795_0.669 528.2631_0.691
## 2 13469.26 126977.6 36765.1 504.0312 7024.1
## 231.0795_0.662 397.2049_0.693 229.0537_0.703 244.908_0.706 246.9051_0.707
## 2 1033.337 10682.89 1605.157 28453.58 26781.31
## 369.1735_0.722 137.0244_0.741 624.3382_0.91 239.0923_0.975 464.3013_0.999
## 2 16647.08 2228.712 2404.195 13168.31 504.0312
## 241.0868_1.048 448.3063_1.326 453.285_1.371 583.2555_1.358 448.3062_1.477
## 2 504.0312 2249.928 504.0312 8213.148 2152.334
## 267.1235_1.539 507.223_1.7 437.2904_2.11 586.3143_2.221 391.2849_2.246
## 2 14312.37 28249.83 2109.412 7916.017 7834.511
## 562.3143_2.27 507.223_2.273 297.1527_2.281 512.2988_2.28 311.2224_2.465
## 2 6131.514 5463.416 4852.787 18104.64 4982.177
## 538.3144_2.488 449.254_2.573 612.33_2.594 640.2923_2.673 656.3173_2.673
## 2 26836.06 3670.256 27236.77 8631.628 11211.98
## 578.3011_2.673 588.3302_2.677 526.3144_2.683 311.1683_2.682 1107.6619_2.7
## 2 11934.06 156921.7 14725.99 10671.61 6479.082
## 517.2436_2.68 524.2776_2.71 830.5005_2.723 1151.649_2.727 504.3089_2.726
## 2 504.0312 17717.52 1502.127 2805.045 18348.54
## 564.3308_2.727 1135.6242_2.73 556.299_2.728 694.2877_2.728 1083.6621_2.728
## 2 396450 2101.62 10870.73 11570.62 15289.02
## 616.2924_2.728 554.3012_2.728 1073.6331_2.728 632.3174_2.729 1602.9935_2.726
## 2 20911.83 28204.33 3184.225 25367.39 1053.242
## 619.2886_2.757 500.2777_2.802 614.3455_2.824 476.2777_2.847 590.3457_2.989
## 2 12485.94 35047.24 7444.078 27902.56 39904.79
## 1521.9681_3.075 670.2878_3.073 1165.6197_3.074 532.2989_3.075 530.3013_3.075
## 2 2808.471 21550.03 6353.824 23154.22 62886.28
## 1025.6334_3.075 592.2925_3.075 540.3313_3.076 608.3174_3.076 1035.6627_3.076
## 2 12987.27 42669.53 1157628 52637.3 93593.68
## 654.31_3.076 1103.6493_3.076 480.309_3.076 1087.6246_3.076 1531.9972_3.076
## 2 8150.14 14690.84 47245.85 10563.33 7595.639
## 1520.9647_3.076 1027.6336_3.076 1530.994_3.076 1598.9806_3.076
## 2 3523.74 6002.621 10113.03 2481.021
## 1315.8467_3.079 820.5155_3.079 1061.6776_3.089 452.2777_3.197 618.308_3.232
## 2 504.0312 2857.127 7589.48 23911.54 14584.35
## 634.3329_3.232 506.3245_3.232 566.3462_3.232 556.3168_3.233 696.3033_3.233
## 2 20147.4 10277.21 273539.3 25799.98 7031.487
## 1087.6933_3.233 558.3148_3.234 478.2933_3.354 524.3351_3.376 526.3505_3.4
## 2 7558.609 8811.278 16215.15 21914.58 18850.23
## 592.3612_3.407 554.3456_3.441 436.2827_3.522 552.3663_3.532 698.3189_3.775
## 2 7309.226 20630.93 13906.46 11245.39 8278.129
## 1159.7117_3.776 1081.6957_3.777 508.3402_3.777 568.3621_3.777 620.3237_3.777
## 2 4318.52 5479.515 16299.13 532299.4 24095.65
## 1143.6869_3.778 636.3486_3.777 558.3325_3.778 1091.7248_3.778 626.3198_3.778
## 2 3467.048 39472.61 42671.38 21538.79 10507
## 560.3303_3.778 1615.0859_3.778 594.3768_3.858 480.309_3.883 599.3194_3.921
## 2 14168.75 2119.961 6830.728 45650.44 15658.1
## 583.2554_3.939 605.2373_3.94 1167.518_3.94 554.3818_4.037 414.0583_4.043
## 2 23206.53 6507.553 5111.501 7755.415 8064.445
## 253.2169_4.044 327.2325_4.055 511.3996_4.087 464.314_4.127 303.2326_4.149
## 2 3715.852 40238.29 3120.451 17825.38 43368.23
## 440.0739_4.207 442.071_4.208 279.2327_4.208 444.0682_4.208 559.4722_4.208
## 2 56946.7 46998.21 129523 17429.46 8758.29
## 463.3422_4.219 537.4153_4.24 539.4304_4.266 539.4306_4.266 573.4515_4.266
## 2 4484.45 33903.05 13237.36 13237.36 11812.46
## 445.3316_4.28 467.3735_4.306 591.462_4.314 445.3316_4.415 539.4309_4.416
## 2 16675.65 24954.8 5188.547 22877.84 10927.55
## 511.3997_4.453 447.3471_4.499 493.389_4.514 447.3471_4.631 473.3629_4.632
## 2 3652.242 11653.17 5953.37 11625.7 5888.98
## 567.4621_4.643 446.0838_4.646 444.0866_4.647 281.2484_4.648 442.0896_4.647
## 2 4465.416 19138.68 50135.16 200246.8 51583.44
## 563.5036_4.648 591.462_4.651 449.3627_4.663 593.4778_4.664 575.467_4.662
## 2 23532.04 6583.166 9165.393 35374.88 3355.543
## 473.3474_4.696 425.363_4.712 441.3942_4.718 491.3733_4.784 818.5544_4.825
## 2 504.0312 12562.76 4651.295 4547.798 504.0312
## 551.3582_4.841 573.4515_4.873 561.3787_4.908 577.3738_4.918 617.4753_4.956
## 2 1659.143 5616.502 8191.66 5274.489 3398.378
## 595.4933_4.953 691.5022_4.977 495.4046_4.993 397.3681_5 717.5179_5.106
## 2 23644.17 11188.64 4821.53 6125.191 27211.07
## 465.3192_5.112 465.3038_5.114 591.3894_5.174 705.5178_5.308 479.3193_5.338
## 2 8263.396 110951.5 11246.6 6977.324 6625.671
## 575.4672_5.354 411.3837_5.349 605.4051_5.422 579.3893_5.557 878.584_5.681
## 2 3909.946 7838.022 6459.481 2674.295 4946.87
## 1394.0685_5.684 787.5209_5.683 709.5047_5.684 719.534_5.684 771.4961_5.685
## 2 4091.168 22534.19 23499.24 222421.6 10974.93
## 711.5035_5.685 849.4911_5.685 493.3349_5.699 722.4967_5.728 904.599_5.836
## 2 9168.952 6182.067 10544.26 3234.964 6464.149
## 1447.1028_5.846 1446.0998_5.845 875.5066_5.845 735.5203_5.846 813.5365_5.846
## 2 8393.435 10199 9896.56 37915.44 33982.08
## 737.5193_5.846 745.5498_5.846 859.5292_5.846 803.5076_5.847 797.5117_5.847
## 2 15724.55 381718.7 6394.41 11439.64 18180.04
## 1271.7465_5.862 848.5434_5.871 822.5279_5.875 748.5122_5.882 824.5433_5.925
## 2 9264.096 5360.774 6334.904 3378.671 5833.091
## 818.554_5.972 818.5539_5.971 721.5493_6 721.549_6 848.5433_6.017
## 2 6304.449 9040.824 8293.607 8293.607 5426.512
## 798.5279_6.032 733.5494_6.091 801.5366_6.09 723.5203_6.092 765.5735_6.098
## 2 26310 126621 13265.09 15401.53 6608.726
## 771.5646_6.096 764.4992_6.1 842.5154_6.099 774.5282_6.1 898.559_6.11
## 2 12778.72 5535.021 6602.767 55365.67 3685.277
## 763.5596_6.181 1068.6675_6.19 801.547_6.254 790.5149_6.254 800.5438_6.256
## 2 13917.59 13256.23 26554.42 8090.822 54200.4
## 868.531_6.255 874.559_6.259 759.5646_6.287 836.5434_6.295 557.4566_6.309
## 2 7316.04 9442.575 9749.275 8236.619 8280.588
## 1605.0902_6.358 1604.0878_6.358 882.5021_6.358 824.5441_6.359 814.5148_6.359
## 2 2503.24 1919.88 6036.377 157509.5 18936
## 892.531_6.359 816.5151_6.359 876.5059_6.36 954.5018_6.361 840.5304_6.407
## 2 16966.92 7578.475 9698.033 5114.851 8459.321
## 850.5595_6.407 790.5148_6.407 918.5466_6.408 800.5433_6.41 868.5308_6.41
## 2 65896.51 6673.638 8493.985 57832.41 6448.348
## 801.547_6.409 812.5436_6.468 884.518_6.476 879.5263_6.489 816.5318_6.478
## 2 29100.63 21515.13 4892.302 5314.663 13349.54
## 818.5302_6.479 956.5168_6.479 894.5465_6.48 1608.119_6.481 826.5598_6.482
## 2 5292.869 4111.511 12525.57 1456.658 111413.3
## 878.5241_6.495 1530.1292_6.506 1529.1254_6.507 896.5849_6.505 906.6148_6.513
## 2 15045.64 7696.467 7073.613 5749.305 25188.61
## 1519.1193_6.539 1491.1091_6.544 747.5662_6.538 1450.1318_6.54 1441.1057_6.541
## 2 10325.66 3698.703 1126504 65371.41 11406.08
## 1518.1184_6.541 1502.0921_6.54 739.5351_6.541 1440.1025_6.541 861.5449_6.542
## 2 12439.59 7361.131 34892.36 13591.3 12531.68
## 799.5274_6.542 831.526_6.542 764.5548_6.543 737.5362_6.543 815.5524_6.543
## 2 40890.11 7634.138 12854.44 91910.27 83607.65
## 877.5224_6.543 1503.101_6.546 788.5437_6.549 805.5234_6.55 883.5394_6.551
## 2 23042.73 8019.273 27396.09 19062.66 13138.25
## 945.5095_6.552 1451.1351_6.539 801.5445_6.55 800.5402_6.543 750.528_6.591
## 2 7267.68 57017.04 18246.8 35417.13 27264.17
## 820.5694_6.598 784.549_6.62 1151.7045_6.635 838.5592_6.651 876.5748_6.674
## 2 5591.081 504.0312 16743.68 3385.642 5824.52
## 814.5592_6.699 773.5806_6.749 763.5516_6.749 841.5661_6.75 1579.1408_6.75
## 2 5335.243 156829.2 17065.55 17132.06 7385.766
## 1656.1198_6.753 850.5603_6.753 1582.1048_6.758 842.5312_6.754 1657.1231_6.754
## 2 8507.573 383956.2 3297.011 17908.62 8909.086
## 853.572_6.759 1646.0906_6.756 840.5306_6.756 902.5217_6.756 867.5493_6.755
## 2 16663.19 2485.114 41751.77 22894.2 6170.334
## 986.5339_6.756 918.5467_6.756 964.5391_6.756 970.4999_6.757 908.5177_6.758
## 2 8439.843 40072.3 5975.645 5105.914 13004.34
## 980.5167_6.758 776.5438_6.762 1647.0986_6.756 844.5327_6.766 766.5148_6.768
## 2 11173.48 87543.61 2520.822 13258.05 8478.303
## 828.5061_6.771 808.5482_6.817 546.1884_6.859 550.1831_6.859 545.3463_6.86
## 2 5257.792 2870.934 30991.66 10394.85 24574.53
## 519.347_6.861 548.1856_6.861 557.457_6.865 559.4719_6.867 543.3083_6.873
## 2 18584.63 31491.55 3774.552 4240.579 8250.871
## 560.2269_6.891 561.2272_6.889 876.5748_6.891 473.3439_6.896 739.5515_6.896
## 2 5078.923 5696.802 8479.144 10678.18 11276.66
## 645.4854_6.897 749.5806_6.906 749.5804_6.907 816.5308_6.915 818.5304_6.915
## 2 6815.698 23554.26 23554.26 92555.02 35193.77
## 946.5031_6.923 843.5493_6.926 884.518_6.929 1600.0951_6.94 962.534_6.937
## 2 8240.493 11779.99 15864.8 7945.235 9319.645
## 878.522_6.938 1599.0952_6.94 1598.0913_6.94 894.5469_6.945 1676.1068_6.945
## 2 34961.79 9671.229 9272.736 70189.63 8662.03
## 956.5171_6.945 940.5392_6.946 1677.1104_6.945 1608.1204_6.95 1609.1238_6.95
## 2 21859.58 10560.02 7693.579 37296.64 38017.94
## 826.5606_6.953 886.5195_6.961 1660.08_6.945 810.5642_6.968 1575.0947_6.977
## 2 828296.4 6658.617 4503.866 2061.912 6851.553
## 1652.1073_6.976 1574.0913_6.977 1584.1204_6.981 1585.1238_6.981
## 2 7466.52 8431.153 34260.91 33486.19
## 795.5319_7.041 1561.1246_7.026 802.5612_7.026 1560.1212_7.025 871.5502_7.054
## 2 14637.81 87123.47 1357794 96113.17 41827.15
## 1551.0949_7.03 1628.1079_7.03 1550.0916_7.031 1629.1111_7.028 917.5422_7.049
## 2 14490.15 14638.6 13662.68 12221.65 8263.755
## 1612.0825_7.034 792.5308_7.046 794.5298_7.047 916.5393_7.053 1610.1346_7.034
## 2 7057.534 86525.65 35337.6 12739.03 22415.33
## 761.5803_7.053 932.5173_7.054 1611.1384_7.055 854.522_7.057 870.547_7.057
## 2 19171.08 23813.22 9165.053 34324.86 84686.48
## 1520.0675_7.067 886.5199_7.016 762.507_7.071 860.5179_7.078 938.534_7.079
## 2 3811.919 8245.688 32796.75 15533.79 10826.1
## 922.5024_7.08 1000.5039_7.081 852.5752_7.104 844.5463_7.11 920.5621_7.108
## 2 7704.432 7322.02 120860.1 6777.977 14412.58
## 842.546_7.109 904.5364_7.114 1586.1348_7.163 878.5901_7.193 720.4962_7.178
## 2 14882.37 7048.357 3374.409 4193.56 5568.113
## 830.5815_7.188 1613.1542_7.188 942.5549_7.193 896.5624_7.191 958.5324_7.191
## 2 39760.63 4131.959 6944.338 34076.44 10640
## 818.5462_7.192 1612.1504_7.187 880.5375_7.198 828.5759_7.19 820.5463_7.196
## 2 34030.33 4982.682 16646.36 269665 14294.41
## 886.5328_7.202 948.5172_7.192 964.5495_7.201 881.5397_7.202 1602.121_7.207
## 2 11507.06 4908.63 8291.848 9636.184 1752.13
## 788.5223_7.225 834.5643_7.242 881.5332_7.243 807.5001_7.254 790.5593_7.252
## 2 3845.78 16588.37 7665.895 5144.873 25775.37
## 864.5747_7.263 738.507_7.269 806.4942_7.271 746.5118_7.267 878.5903_7.305
## 2 7477.013 39627.31 5332.414 3596.26 7596.104
## 840.5749_7.321 1602.1228_7.339 948.5173_7.337 886.5331_7.335 897.5657_7.337
## 2 16394.42 3432.839 8086.667 16540 28673.65
## 1026.5195_7.338 964.5495_7.339 818.5462_7.337 958.5325_7.337 942.5548_7.339
## 2 7702.331 12165.59 55049.56 15019.36 9148.164
## 1603.1254_7.34 880.5376_7.34 828.5759_7.339 896.5624_7.34 820.5462_7.34
## 2 4002.357 25622.81 430864.8 51577.68 22564.87
## 1612.1508_7.34 845.5636_7.343 1637.1538_7.351 1613.1545_7.34 912.5357_7.346
## 2 10090.17 8650.325 3598.205 10747.9 5677.158
## 714.507_7.348 1636.1506_7.352 920.5621_7.373 854.5867_7.421 881.5405_7.337
## 2 21358.97 3177.651 9580.489 18861.1 12995.19
## 852.5752_7.369 842.546_7.377 904.5361_7.374 843.5493_7.379 836.5799_7.404
## 2 69645.93 9225.879 5982.632 5791.627 12651.91
## 764.5225_7.43 857.5176_7.442 800.5354_7.468 810.5647_7.467 878.5516_7.469
## 2 12744.66 32854.14 17461.85 132522.2 17583.55
## 840.5749_7.471 862.527_7.471 868.5303_7.476 862.5261_7.47 800.5357_7.469
## 2 30003.94 10110.18 6806.371 10110.18 17461.85
## 922.5767_7.503 854.5896_7.505 740.5225_7.515 833.5175_7.518 855.5937_7.505
## 2 5766.143 29425.29 7085.379 24162.94 17361.94
## 862.5953_7.53 884.5607_7.548 776.5354_7.574 816.5751_7.553 806.5463_7.553
## 2 7405.017 8423.698 11808.4 44148.65 6009.256
## 833.5544_7.557 884.5587_7.554 843.5839_7.561 767.5664_7.562 905.5552_7.563
## 2 10528.56 7110.306 32967.96 12791.67 10971.17
## 765.5672_7.563 775.5965_7.563 786.5646_7.575 854.5519_7.576 776.5351_7.576
## 2 32845.13 235038.5 73149.23 8554.003 11808.4
## 778.5595_7.588 768.5305_7.588 830.5218_7.589 846.5477_7.59 836.5799_7.605
## 2 106386.6 13095.34 6905.696 15131.24 24562.97
## 883.5333_7.614 746.5121_7.622 814.4993_7.622 812.5802_7.632 864.5398_7.662
## 2 6894.407 42279.55 5209.321 90708.62 7775.516
## 802.5529_7.634 880.5669_7.637 1179.7355_7.664 859.5339_7.699 855.5938_7.701
## 2 15428.46 14540.64 6533.578 4189.126 24039.59
## 764.5224_7.695 922.5774_7.695 854.5906_7.701 788.5795_7.719 856.5627_7.726
## 2 5594.565 7022.352 41821.79 29939.32 5997.815
## 828.5728_7.674 906.5814_7.742 838.5956_7.75 1598.1714_7.758 864.535_7.755
## 2 18499.06 7983.931 54082.33 5964.213 9644.078
## 883.5331_7.753 862.5335_7.761 940.5496_7.764 1616.1136_7.768 924.518_7.766
## 2 6612.639 20426.83 13661.23 4574.519 10243.37
## 888.5362_7.768 821.565_7.768 1633.1422_7.775 872.5626_7.769 1002.5196_7.769
## 2 7455.208 11493.69 8331.001 87200.16 6726.316
## 856.5377_7.769 918.5543_7.768 796.5458_7.771 1555.1258_7.773 934.5326_7.772
## 2 38901.9 13405.03 32951.06 8986.03 24030.76
## 794.5464_7.772 1554.1226_7.773 804.5762_7.774 918.5546_7.77 857.5409_7.768
## 2 84274.06 9048.828 816784.6 13405.03 20997.44
## 1564.1517_7.775 1632.139_7.776 1565.1551_7.774 772.5276_7.782 1639.1701_7.792
## 2 35523.35 7901.544 31104.63 13494.5 7250.444
## 1638.1667_7.792 878.5909_7.805 936.5439_7.803 1008.5435_7.813 870.5618_7.814
## 2 6213.194 86858.23 5752.748 4836.889 5132.105
## 946.5777_7.814 868.5616_7.814 930.5504_7.815 1482.0881_7.834 801.6117_7.833
## 2 11800.33 11311.25 5586.249 4683.703 72313.08
## 869.598_7.834 791.5828_7.838 824.5789_7.841 814.5949_7.87 864.6108_7.878
## 2 10249.83 12324.84 3535.81 13830.42 3409.736
## 722.5123_7.861 790.4993_7.863 820.4801_7.866 780.4705_7.867 814.595_7.869
## 2 115125.7 12781.59 6431.214 6948.083 13830.42
## 859.5338_7.864 814.5947_7.872 842.5905_7.891 827.6272_7.905 812.58_7.898
## 2 5668.923 13830.42 7701.905 22540.65 14386.56
## 789.6114_7.911 792.5765_7.938 698.5121_7.962 777.6118_7.986 1558.1192_8.016
## 2 5187.014 5050.927 17849.77 14208.75 3159.345
## 1052.5353_8.011 990.5651_8.013 974.5321_8.013 912.5491_8.014 748.5277_8.014
## 2 8414.304 12534.17 7662.098 20708.59 46733.69
## 906.5531_8.016 922.5781_8.016 871.5805_8.017 846.5617_8.018 1655.1569_8.02
## 2 29787.68 73024.2 10395.63 28928.26 5415.9
## 984.5483_8.018 1654.1535_8.02 938.5513_8.018 968.5707_8.019 844.562_8.02
## 2 18987.99 5260.174 5926.185 11094.87 72785.15
## 1665.186_8.021 854.5918_8.021 1664.1825_8.021 914.5508_8.03 724.5275_8.059
## 2 15773.8 597630.8 16215.74 9704.4 6238.598
## 1631.1566_8.057 814.5957_8.055 1630.1533_8.057 1641.1859_8.06 1640.1826_8.061
## 2 4667.314 6033.972 3949.361 11569 13329.37
## 1616.183_8.109 945.5734_8.118 1685.1732_8.11 830.5921_8.11 1617.1865_8.109
## 2 30387.21 9049.975 8840.793 810380.8 33352.45
## 1684.1698_8.112 1607.1572_8.113 1606.1538_8.113 880.6062_8.116
## 2 8998.021 9455.198 8503.058 24042.25
## 1668.1442_8.113 944.5705_8.116 822.5616_8.116 820.562_8.117 960.5481_8.117
## 2 5463.825 13576.03 37789.98 85715.25 24214.5
## 847.5799_8.118 898.5782_8.119 853.6428_8.12 882.5533_8.121 883.5565_8.118
## 2 12919.27 89561.43 24748.56 37676.09 19931.18
## 716.5225_8.139 888.549_8.143 914.5514_8.091 950.5333_8.144 890.5493_8.144
## 2 7779.829 23152.95 9903.89 9753.938 10276.3
## 1028.5349_8.145 724.5277_8.141 966.5651_8.147 724.5275_8.142 790.5382_8.16
## 2 8666.269 7835.78 18252.37 7835.78 21106.93
## 789.6116_8.177 840.6105_8.214 762.5643_8.206 856.6051_8.21 582.5094_8.227
## 2 11812.96 4544.349 18487.79 13209.35 10990.91
## 572.4806_8.229 574.4787_8.23 838.5955_8.279 748.5276_8.299 724.5276_8.3
## 2 21580.78 7488.744 8260.981 8657.788 4696.703
## 835.5322_8.336 764.5798_8.343 818.5906_8.346 862.5953_8.355 909.5492_8.36
## 2 16368.27 15757.45 17764.04 6502.125 8638.559
## 902.5128_8.383 774.5431_8.358 824.4968_8.383 834.5254_8.385 766.5385_8.385
## 2 4093 2007.099 8713.037 16660 102631.6
## 864.5064_8.385 788.58_8.386 815.627_8.417 736.5275_8.432 1669.2155_8.46
## 2 8448.367 22432.65 9359.73 4345.761 1621.755
## 1668.2129_8.463 840.6112_8.445 810.5275_8.448 841.6406_8.452 976.5477_8.457
## 2 2019.322 9788.199 33076.27 11286.62 3996.818
## 914.5643_8.457 848.577_8.458 846.5774_8.459 986.5637_8.459 908.5688_8.459
## 2 9376.798 11834.77 28534.22 8202.189 13272.88
## 992.5806_8.459 924.5935_8.459 970.5858_8.459 856.6068_8.46 873.5954_8.46
## 2 6013.534 27763.82 5290.129 152973.3 5111.065
## 742.5383_8.471 792.5536_8.473 1697.1555_8.491 885.5495_8.509 790.5953_8.511
## 2 33108.33 5459.144 2762.226 219215 23723.5
## 953.536_8.516 1015.5064_8.516 880.6062_8.517 983.5161_8.518 1021.5212_8.523
## 2 26731.98 7852.93 12420.57 12558.52 8244.566
## 943.5073_8.524 1005.4968_8.528 864.6108_8.544 828.5668_8.612 838.5956_8.612
## 2 11931.81 10613.68 4830.832 5983.636 25094.97
## 906.5828_8.614 929.5358_8.639 861.5489_8.641 911.5645_8.686 793.5985_8.748
## 2 5728.307 6024.246 37498.08 4402.336 19151.19
## 803.6275_8.749 750.5432_8.694 871.6147_8.752 844.6062_8.707 814.5957_8.732
## 2 103716.2 12871.62 20451.34 5184.281 13738.55
## 806.5905_8.734 806.5895_8.732 804.6308_8.768 855.59_8.755 861.5846_8.743
## 2 18060.45 17002.7 49663 12279.39 10165.64
## 795.5979_8.768 793.5985_8.765 842.5306_8.773 774.5433_8.775 803.6275_8.771
## 2 9425.681 21094.18 5530.655 35749.25 103716.2
## 871.6147_8.768 933.5855_8.781 840.6113_8.8 830.5838_8.802 908.5977_8.803
## 2 20451.34 6936.892 35263.41 7860.876 8485.741
## 700.5277_8.805 819.6141_8.816 829.6431_8.816 821.6152_8.817 856.6029_8.818
## 2 8064.323 12680.54 59890.45 5772.978 8446.201
## 887.599_8.816 768.554_8.827 804.6307_8.782 882.6209_8.855 872.5955_8.863
## 2 5530.63 6555.804 49663 14926.71 3023.773
## 950.6082_8.859 866.6268_8.879 800.5587_8.868 816.6106_8.908 968.581_8.937
## 2 3764.793 8463.001 2290.457 9929.165 6303.916
## 946.5851_8.939 824.5771_8.938 962.5631_8.939 890.5649_8.939 900.5936_8.939
## 2 4725.83 11668.07 7632.191 10249.58 28638.6
## 832.6067_8.94 884.5687_8.94 822.5774_8.941 897.6305_9.024 829.6432_9.024
## 2 144460.3 13710.05 27029.9 28433.56 172523.2
## 881.6055_9.023 819.6141_9.024 959.6018_9.026 1561.2028_9.038 821.6139_9.026
## 2 13610.34 29945.22 9545.775 3594.852 13116.51
## 887.5991_9.03 847.6304_9.032 903.5878_9.033 855.6591_9.033 969.6384_9.033
## 2 9205.066 16887.53 6131.223 210398.3 6098.171
## 985.616_9.034 991.6331_9.034 907.6211_9.035 923.646_9.035 913.6162_9.035
## 2 9650.048 8398.318 20509.09 40109.94 13624.29
## 845.6298_9.037 750.5436_9.042 818.5306_9.045 848.5114_9.046 808.5018_9.051
## 2 40144.54 111689.3 14247.46 7089.536 6832.379
## 888.5678_9.068 887.5646_9.068 776.5589_9.132 726.5434_9.157 794.5306_9.158
## 2 14691.85 23708.07 13893.18 22353 4055.496
## 858.622_9.163 858.6217_9.164 752.5589_9.222 805.6426_9.222 831.6576_9.28
## 2 11971.49 12272.23 5368.922 5129.886 4040.853
## 744.5538_9.321 833.6294_9.395 843.6586_9.396 911.6461_9.397 875.6844_9.397
## 2 7145.488 9015.579 34686.9 7792.042 6487.938
## 788.5437_9.413 817.643_9.423 885.6305_9.426 807.6141_9.427 887.5646_9.463
## 2 18918.13 35230.9 8296.173 8985.067 3790
## 600.5119_9.48 752.5591_9.514 857.6737_9.513 931.5514_9.582 863.5644_9.582
## 2 6794.859 3570.447 4379.644 2323.091 10587.6
## 911.6459_9.666 901.6169_9.665 895.6211_9.665 833.6297_9.665 843.6587_9.666
## 2 13474.35 6688.284 9071.03 16480.4 60810.84
## 911.646_9.667 1016.7241_9.858 983.6184_9.976 778.5744_9.917 889.617_9.974
## 2 13474.35 4702.815 5785.703 7963.854 13294.15
## 945.6385_9.975 967.6322_9.976 1670.349_9.987 883.6211_9.976 821.6298_9.976
## 2 5831.862 8264.824 4279.315 18760.4 40503.67
## 961.6178_9.977 899.6459_9.978 823.6294_9.979 831.659_9.98 1644.3337_9.984
## 2 11646.76 41054.9 16734.79 224299.8 6377.426
## 993.6488_9.986 857.6749_9.986 971.6542_9.988 915.6319_9.987 987.6318_9.987
## 2 10976.12 269686.7 7963.251 17955.48 12177.78
## 925.6617_9.988 847.6455_9.988 849.6462_9.988 917.6329_9.989 874.6643_9.99
## 2 48529.55 49363.1 23903.68 7906.335 7891.789
## 905.6028_9.99 909.6368_9.99 931.6162_9.992 983.6185_9.993 728.5589_10.045
## 2 8745.66 23330.14 5534.356 5785.703 5464.533
## 931.6162_9.999 653.491_10.071 1261.8136_10.084 1235.798_10.088 894.658_10.086
## 2 5534.356 5309.998 6648.756 6083.29 6683.017
## 860.6369_10.19 778.5745_10.284 993.6487_10.29 925.6615_10.291 915.6327_10.293
## 2 4347.889 16747.36 9180.627 42885.46 17120.49
## 917.633_10.292 857.6746_10.294 849.6458_10.294 967.594_10.294 931.6164_10.288
## 2 8375.145 227623.9 29497.33 8381.064 5103.214
## 909.6366_10.295 847.6455_10.298 905.6039_10.298 874.6642_10.298
## 2 31145.99 68079.23 10383.8 12174.11
## 920.6735_10.31 871.6891_10.337 939.6769_10.335 923.6524_10.353
## 2 11407.93 23431.19 4883.789 4365.229
## 1261.8135_10.352 754.5745_10.357 833.6742_10.379 860.6919_10.384
## 2 6244.344 5013.834 22248.72 13928.97
## 823.6447_10.383 818.6271_10.414 828.6559_10.414 820.627_10.414
## 2 10651.29 49472.3 36236.82 24093.94
## 854.6714_10.418 1646.3475_10.439 904.6327_10.443 913.6613_10.438
## 2 23159.98 3366.365 10757.52 28688.91
## 1637.3241_10.442 845.6746_10.439 1636.3208_10.44 903.6327_10.442
## 2 4831.971 248822.1 6311.003 18021.23
## 897.637_10.442 905.6301_10.443 837.6454_10.443 835.6457_10.444 862.664_10.445
## 2 38940.5 8713.711 52024.61 129008.9 25511.13
## 955.5948_10.447 920.6223_10.449 893.604_10.45 895.6025_10.452 816.5752_10.467
## 2 9650.611 7492.464 18636.53 13528.38 5412.694
## 1249.8137_10.468 638.5721_10.489 628.5437_10.491 630.5417_10.49
## 2 8322.147 11466.03 21496.66 7993.306
## 1279.8241_10.52 1018.7397_10.524 1008.7111_10.525 654.5589_10.54
## 2 8390.591 5776.77 7643.005 26564.16
## 656.5586_10.54 664.5877_10.541 682.5753_10.545 680.5744_10.545 707.593_10.547
## 2 10790.82 15001.57 15388.22 33780.41 8583.865
## 690.603_10.546 936.6574_10.551 832.6428_10.551 842.6715_10.551
## 2 16967.46 4246.165 43308.28 19995.17
## 946.6887_10.548 837.6571_10.552 864.6677_10.558 1263.8295_10.575
## 2 3736.578 5951.105 12317.85 17800.06
## 885.7055_10.58 862.6535_10.583 872.6815_10.583 911.652_10.584 1675.384_10.589
## 2 5392.283 8905.027 6678.577 34630.83 6900.487
## 969.6092_10.587 1674.3803_10.59 1666.3553_10.589 859.6907_10.59
## 2 6806.572 5760.724 9989.021 372254.8
## 849.6616_10.589 1665.3553_10.589 927.677_10.59 876.68_10.59 851.6615_10.59
## 2 232448.2 12303.33 27190.55 45172.95 98275.59
## 917.6482_10.589 1664.3521_10.59 919.6483_10.59 907.6204_10.599 909.6189_10.6
## 2 18074.47 10063.39 8632.195 22921.64 15571.64
## 934.6384_10.6 912.6573_10.608 652.5879_10.611 642.5594_10.614 806.6057_10.613
## 2 9350.669 26177.18 7656.03 9989.44 4466.742
## 922.6891_10.613 896.6737_10.623 914.6592_10.621 970.6153_10.616
## 2 13885.93 7693.493 8838.573 5592.268
## 886.645_10.627 856.6871_10.641 874.6819_10.641 846.6588_10.643
## 2 6484.797 41352.64 9696.189 105766.6
## 908.6381_10.644 695.5929_10.644 678.6034_10.645 904.6171_10.646
## 2 11053.44 6063.055 12335.33 8930.509
## 670.5743_10.646 668.5747_10.647 853.619_10.659 865.6758_10.678
## 2 7433.114 18218.18 7036.017 7932.888
## 863.6765_10.683 851.6659_10.671 658.5736_10.694 656.575_10.694
## 2 17536.23 16954.31 50116.99 136334.9
## 666.6032_10.695 744.5811_10.699 684.591_10.697 682.5904_10.698
## 2 80462.2 5573.494 47335.47 121164.5
## 692.6191_10.698 709.609_10.707 640.5796_10.729 676.6243_10.731
## 2 66447.8 23455.6 2462.641 3118.647
## 696.6064_10.749 706.6345_10.754 658.5898_10.758 660.5885_10.757
## 2 11036.68 7231.183 23411.76 8107.94
## 668.6174_10.756 855.6346_10.759 698.6108_10.776 697.6094_10.776
## 2 15633.95 8517.014 12544.25 27215.15
## 670.5906_10.779 680.6191_10.777 672.5896_10.777 674.6039_10.834
## 2 125584.7 62872.69 44991.65 5083.685
## 682.6332_10.835 1335.245_10.854 694.635_10.854 684.6065_10.853
## 2 9162.783 4942.82 245587 354283.5
## 686.6054_10.854 746.5966_10.853 1334.2417_10.855 762.6216_10.854
## 2 148662 11560.14 5303.952 4838.753
## 711.6251_10.854 652.6239_10.859 678.6402_10.885 668.611_10.888
## 2 61655.19 3495.972 5296.919 3014.234
## 698.6217_10.912 700.6211_10.914 708.6502_10.915 686.6207_10.914
## 2 34974.13 15591.54 20487.36 31390.26
## 697.653_10.915 688.6203_10.915 696.6498_10.915 725.6412_10.916
## 2 11392.33 12834.53 21661.52 8151.158
## 722.6659_11.002 680.6552_11.016 960.7412_11.038 986.7566_11.049
## 2 5985.15 3538.826 9043.629 11070.37
## 988.7722_11.162 1014.7875_11.17
## 2 11237.98 6091.959
connection <- find_connections(data = data_notame,
features = features,
corr_thresh = 0.95,
rt_window = 1/60,
name_col = "mz_rt",
mz_col = "mz",
rt_col = "rt")## [1] 100
## [1] 200
## [1] 300
## [1] 400
## [1] 500
## [1] 600
## [1] 700
## [1] 800
## [1] 900
## [1] 1000
## x y cor rt_diff mz_diff
## 1 191.0197_0.621 111.0086_0.62 0.9588214 -0.001 -80.0111
## 2 187.0418_0.642 89.0243_0.645 0.9844393 0.003 -98.0175
## 3 479.0972_0.649 187.007_0.66 0.9591729 0.011 -292.0902
## 4 479.0972_0.649 107.0502_0.661 0.9559836 0.012 -372.0470
## 5 187.007_0.66 107.0502_0.661 0.9879347 0.001 -79.9568
## 6 244.908_0.706 246.9051_0.707 0.9675337 0.001 1.9971
## 58 components found
##
## 14 components found
##
## 1 components found
# assign a cluster ID to all features. Clusters are named after feature with highest median peak height
features_clustered <- assign_cluster_id(data_notame, clusters, features, name_col = "mz_rt")
# lets see how many features are removed when we only keep one feature per cluster
pulled <- pull_clusters(data_notame, features_clustered, name_col = "mz_rt")
cluster_data <- pulled$cdata
cluster_features <- pulled$cfeatures
# how much did we trim our data down by?
nrow(omicsdata) - nrow(cluster_features)## [1] 81
# transpose the full dataset back to wide so that it is more similar to the notame dataset
imp_meta_omics_wide <- imp_meta_omics %>%
dplyr::select(-"row_id") %>%
pivot_wider(names_from = mz_rt,
values_from = peak_height)
# list of reduced features
clusternames <- cluster_features$mz_rt
# select only the features are in the reduced list
imp_clust <- imp_meta_omics_wide[,c(names(imp_meta_omics_wide) %in% clusternames)]
# bind back sample names
imp_clust <- cbind(imp_meta_omics_wide[1], imp_clust)
tibble(imp_clust)## # A tibble: 85 × 955
## sample `272.9587_0.485` `288.9363_0.485` `226.9658_0.499` `294.9532_0.5`
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 x5101_b1_c… 11987. 21535. 28761. 22339.
## 2 x5105_b3_l… 15548. 23409. 32647. 24825.
## 3 x5112_b1_b… 14778. 19901. 31099. 23495.
## 4 x5107_b1_b… 13083. 21959. 31327. 24307.
## 5 x5105_b1_l… 14824. 23221. 35289. 23222.
## 6 x5102_b1_c… 15760. 24398. 30828. 22992.
## 7 x5101_b3_c… 13294. 20615. 33028. 25811.
## 8 x5103_b1_l… 13628. 19189. 26919. 22856.
## 9 x5104_b3_l… 12694. 18564. 30674. 25084.
## 10 x5109_b3_b… 12346. 22480. 31363. 23632.
## # ℹ 75 more rows
## # ℹ 950 more variables: `362.9406_0.501` <dbl>, `520.9099_0.514` <dbl>,
## # `588.8973_0.514` <dbl>, `452.9225_0.515` <dbl>, `656.8848_0.515` <dbl>,
## # `724.8722_0.515` <dbl>, `792.8596_0.515` <dbl>, `384.9351_0.516` <dbl>,
## # `316.9477_0.518` <dbl>, `604.8712_0.518` <dbl>, `248.9604_0.52` <dbl>,
## # `215.0328_0.588` <dbl>, `217.0297_0.589` <dbl>, `151.0261_0.612` <dbl>,
## # `335.0471_0.599` <dbl>, `649.1192_0.601` <dbl>, `167.021_0.602` <dbl>, …
Let’s see how our clustered data looks now compared to the original
# plot new rt vs mz scatterplot post-clustering
(plot_mzvsrt_postcluster <- cluster_features %>%
ggplot(aes(x = rt,
y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "Retention time, min",
y = "m/z, neutral",
title = "mz across RT for all features after clustering"))# plot both scatterplots to compare with and without notame clustering
(scatterplots <- ggarrange(plot_mzvsrt,
plot_mzvsrt_postcluster,
nrow = 2))# meta data columns
str_meta <- colnames(imp_metabind_clust[,4:13])
# change meta data columns to character so that I can change NAs from QCs to "QC"
imp_metabind_clust <- imp_metabind_clust %>%
mutate_at(str_meta, as.character)
# replace NAs in metadata columns for QCs
imp_metabind_clust[is.na(imp_metabind_clust)] <- "QC"
# long df
imp_metabind_clust_tidy <- imp_metabind_clust %>%
pivot_longer(cols = 15:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund")
# structure check
head(imp_metabind_clust_tidy)## # A tibble: 6 × 16
## subject treatment tomato_or_control sex bmi age tot_chol ldl_chol
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 5107 beta tomato M 23.81 68 177 85.4
## 2 5107 beta tomato M 23.81 68 177 85.4
## 3 5107 beta tomato M 23.81 68 177 85.4
## 4 5107 beta tomato M 23.81 68 177 85.4
## 5 5107 beta tomato M 23.81 68 177 85.4
## 6 5107 beta tomato M 23.81 68 177 85.4
## # ℹ 8 more variables: hdl_chol <chr>, triglycerides <chr>, glucose <chr>,
## # SBP <chr>, DBP <chr>, sample <chr>, mz_rt <chr>, rel_abund <dbl>
imp_metabind_clust_tidy %>%
ggplot(aes(x = subject, y = rel_abund, color = treatment)) +
geom_boxplot(alpha = 0.6) +
scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "LC-MS (+) Feature Abundances by Sample",
subtitle = "Unscaled data",
y = "Relative abundance")Will need to log transform in order to normalize and actually see the data
imp_metabind_clust_tidy_log2 <- imp_metabind_clust_tidy %>%
mutate(rel_abund_log2 = log2(rel_abund))(bp_data_quality <- imp_metabind_clust_tidy_log2 %>%
ggplot(aes(x = sample, y = rel_abund_log2, color = treatment)) +
geom_boxplot(alpha = 0.6) +
scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "LC-MS (+) Feature Abundances by Sample",
subtitle = "Log2 transformed data",
y = "Relative abundance"))# filtered and imputed data after notame clustering, transposed
features_forQCcorr <- imp_clust %>%
dplyr::select(!subject) %>%
t() %>%
as.data.frame() %>%
row_to_names(row_number = "find_header")
# log2 transform
log2_features_forQCcorr <- features_forQCcorr %>%
mutate_all(as.numeric) %>%
log2()
# write csv to manually edit
write.csv(log2_features_forQCcorr,
"Notame/feaures_fromR_forDC_1.csv",
row.names = TRUE)Import corrected df (edited so that “mz_rt” could be the row name for row 1)
log2_features_forQCcorr_new <- read.csv("Notame/feaures_forR_forDC_editedmzrt_2.csv",
header = FALSE,
row.names = 1)
log2_features_forQCcorr_new <- log2_features_forQCcorr_new %>%
rownames_to_column(var = "mz_rt") %>%
row_to_names(row_number = 1) %>%
separate(col = mz_rt,
into = c("mz", "rt"),
sep = "_")
write.csv(log2_features_forQCcorr_new,
"Notame/features_fromR_forDC_3.csv",
row.names = TRUE)# separate sampleID and injection order
pheno_data <- imp_clust[1] %>%
separate(col = sample,
into = c("sample", "injection_order"),
# last underscore
sep = "_(?!.*_)")
# make inj order column numeric
pheno_data <- pheno_data %>%
mutate_at("injection_order", as.numeric)
t_pheno_data <- as.data.frame(t(pheno_data))
write.csv(t_pheno_data,
"Notame/pheno_df.csv",
row.names = TRUE)Combine pheno and feature dfs manually in excel to create metaboset df.
#make sure when converting csv to xlsx that you save as a new file, don't just change the name of the file
metaboset <- read_from_excel("Notame/metaboset.xlsx",
split_by = c("column", "Ion mode"))## INFO [2025-09-30 18:38:00] Detecting corner position
## INFO [2025-09-30 18:38:00] Corner detected correctly at row 3, column D
## INFO [2025-09-30 18:38:00]
## Extracting sample information from rows 1 to 3 and columns E to CK
## INFO [2025-09-30 18:38:00] Replacing spaces in sample information column names with underscores (_)
## INFO [2025-09-30 18:38:00] Naming the last column of sample information "Datafile"
## INFO [2025-09-30 18:38:00]
## Extracting feature information from rows 4 to 957 and columns A to D
## INFO [2025-09-30 18:38:00] Creating Split column from column, Ion mode
## INFO [2025-09-30 18:38:00] Feature_ID column not found, creating feature IDs
## INFO [2025-09-30 18:38:00] Identified m/z column mass and retention time column rt
## INFO [2025-09-30 18:38:00] Identified m/z column mass and retention time column rt
## INFO [2025-09-30 18:38:00] Creating feature IDs from Split, m/z and retention time
## INFO [2025-09-30 18:38:01] Replacing dots (.) in feature information column names with underscores (_)
## INFO [2025-09-30 18:38:01]
## Extracting feature abundances from rows 4 to 957 and columns E to CK
## INFO [2025-09-30 18:38:01]
## Checking sample information
## INFO [2025-09-30 18:38:01] QC column generated from rows containing 'QC'
## INFO [2025-09-30 18:38:01] Sample ID autogenerated from injection orders and prefix ID_
## INFO [2025-09-30 18:38:01] Checking that feature abundances only contain numeric values
## INFO [2025-09-30 18:38:01]
## Checking feature information
## INFO [2025-09-30 18:38:01] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-09-30 18:38:01] Checking that m/z and retention time values are reasonable
## INFO [2025-09-30 18:38:01] Identified m/z column mass and retention time column rt
## INFO [2025-09-30 18:38:01] Identified m/z column mass and retention time column rt
#construct Metaboset
modes <- construct_metabosets(exprs = metaboset$exprs,
pheno_data = metaboset$pheno_data,
feature_data = metaboset$feature_data, group_col = "Class")## Initializing the object(s) with unflagged features
## INFO [2025-09-30 18:38:01]
## Checking feature information
## INFO [2025-09-30 18:38:01] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-09-30 18:38:01] Checking that feature abundances only contain numeric values
## INFO [2025-09-30 18:38:01] Setting row and column names of exprs based on feature and pheno data
# ordered by injection
(qualityBPs_b4correction <- plot_sample_boxplots(mode, order_by = c("Class", "Injection_order"), title = "Uncorrected feature abundance"))#ordered by class
plot_sample_boxplots(mode, order_by = "Injection_order", title = "Uncorrected feature abundance")drift correction takes up to 2 minutes
## INFO [2025-09-30 18:38:05]
## 0% of features flagged for low detection rate
## INFO [2025-09-30 18:38:05]
## Starting drift correction at 2025-09-30 18:38:05.815966
## INFO [2025-09-30 18:38:09] Drift correction performed at 2025-09-30 18:38:09.396701
## INFO [2025-09-30 18:38:11] Inspecting drift correction results 2025-09-30 18:38:11.026608
## INFO [2025-09-30 18:38:15] Drift correction results inspected at 2025-09-30 18:38:15.678307
## INFO [2025-09-30 18:38:15]
## Drift correction results inspected, report:
## Drift_corrected: 100%
output is percent of the features that were drift corrected. The remaining “low-quality” percent represents features for which the DC did not improve the RSD and D-ratio of the original data.
## INFO [2025-09-30 18:38:20] Inspecting drift correction results 2025-09-30 18:38:20.057847
## INFO [2025-09-30 18:38:26] Drift correction results inspected at 2025-09-30 18:38:26.226664
## INFO [2025-09-30 18:38:26]
## Drift correction results inspected, report:
## Drift_corrected: 59%, Low_quality: 41%
(qualityBPs_compared <- ggarrange(qualityBPs_b4correction, qualityBPS_driftcorrection,
ncol = 1, nrow = 2))Manually edit the df so it only has mass, rt, and sample columns
metabdata_corrected_MZ_RT <- metabdata_corrected %>%
mutate(mass = round(metabdata_corrected$mass, digits = 4), # Decrease number of decimals for m/z & rt
rt = round(metabdata_corrected$rt, digits = 3),
.before=1,
.keep="unused") %>%
unite(mz_rt, c(mass, rt), remove=TRUE) # Combine m/z & rt with _ in betweenI want the new “metabdata_corrected_t” df to look just like “imp_metabind_clust_log2” df
# go back to wide data
imp_metabind_clust_log2 <- imp_metabind_clust_tidy_log2 %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)# bind metadata columns to the new drift corrected df
DC_imp_metabind_clust_log2 <- full_join(imp_metabind_clust_log2[,c(1:14)],
metabdata_corrected_t,
by = "sample")
# fix QC subject names
DC_imp_metabind_clust_log2$subject <- str_replace_all(DC_imp_metabind_clust_log2$subject, "c", "qc")
DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
mutate_at("subject", str_sub, start=1, end=4)
# make feature abundances numeric
DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
mutate_at(15:ncol(.), as.numeric)PCA.DC_imp_metabind_clust_log2 <- PCA(DC_imp_metabind_clust_log2, # wide data
quali.sup = 1:14, # remove qualitative variables
graph = FALSE, # don't graph
scale.unit = FALSE) # don't scale, already transformed data
# PCA summary
kable(summary(PCA.DC_imp_metabind_clust_log2))##
## Call:
## PCA(X = DC_imp_metabind_clust_log2, scale.unit = FALSE, quali.sup = 1:14,
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 19.842 16.531 13.524 12.302 11.065 8.404 7.496
## % of var. 11.452 9.541 7.805 7.100 6.386 4.850 4.326
## Cumulative % of var. 11.452 20.993 28.798 35.898 42.284 47.134 51.460
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 6.892 6.245 5.624 4.849 4.459 3.990 3.607
## % of var. 3.978 3.604 3.246 2.799 2.573 2.303 2.082
## Cumulative % of var. 55.438 59.042 62.288 65.087 67.660 69.963 72.045
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 3.115 3.029 2.830 2.559 2.397 2.150 2.001
## % of var. 1.798 1.748 1.633 1.477 1.384 1.241 1.155
## Cumulative % of var. 73.842 75.590 77.224 78.701 80.084 81.325 82.480
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 1.931 1.852 1.567 1.413 1.359 1.247 1.189
## % of var. 1.114 1.069 0.905 0.816 0.784 0.720 0.686
## Cumulative % of var. 83.594 84.663 85.568 86.383 87.167 87.887 88.574
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 1.135 1.103 0.953 0.946 0.853 0.824 0.790
## % of var. 0.655 0.636 0.550 0.546 0.492 0.476 0.456
## Cumulative % of var. 89.229 89.865 90.416 90.962 91.454 91.930 92.385
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.727 0.723 0.637 0.618 0.575 0.541 0.499
## % of var. 0.420 0.417 0.368 0.357 0.332 0.312 0.288
## Cumulative % of var. 92.805 93.222 93.590 93.947 94.278 94.590 94.878
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 0.476 0.446 0.428 0.423 0.389 0.382 0.342
## % of var. 0.275 0.258 0.247 0.244 0.225 0.220 0.197
## Cumulative % of var. 95.153 95.411 95.658 95.902 96.127 96.347 96.545
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54 Dim.55 Dim.56
## Variance 0.336 0.322 0.309 0.294 0.277 0.272 0.263
## % of var. 0.194 0.186 0.179 0.170 0.160 0.157 0.152
## Cumulative % of var. 96.739 96.925 97.103 97.273 97.433 97.590 97.742
## Dim.57 Dim.58 Dim.59 Dim.60 Dim.61 Dim.62 Dim.63
## Variance 0.249 0.240 0.231 0.219 0.204 0.196 0.192
## % of var. 0.144 0.139 0.133 0.127 0.118 0.113 0.111
## Cumulative % of var. 97.886 98.024 98.157 98.284 98.402 98.515 98.625
## Dim.64 Dim.65 Dim.66 Dim.67 Dim.68 Dim.69 Dim.70
## Variance 0.185 0.170 0.162 0.157 0.148 0.147 0.140
## % of var. 0.107 0.098 0.093 0.090 0.085 0.085 0.081
## Cumulative % of var. 98.732 98.830 98.924 99.014 99.099 99.184 99.265
## Dim.71 Dim.72 Dim.73 Dim.74 Dim.75 Dim.76 Dim.77
## Variance 0.137 0.126 0.125 0.119 0.109 0.106 0.100
## % of var. 0.079 0.072 0.072 0.069 0.063 0.061 0.057
## Cumulative % of var. 99.344 99.417 99.489 99.558 99.621 99.682 99.739
## Dim.78 Dim.79 Dim.80 Dim.81 Dim.82 Dim.83 Dim.84
## Variance 0.092 0.088 0.082 0.080 0.074 0.036 0.000
## % of var. 0.053 0.051 0.047 0.046 0.043 0.021 0.000
## Cumulative % of var. 99.792 99.843 99.890 99.936 99.979 100.000 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 20.533 | -1.184 0.083 0.003 | -2.038 0.296 0.010
## 2 | 16.185 | 3.466 0.712 0.046 | 3.498 0.871 0.047
## 3 | 14.348 | -5.882 2.051 0.168 | 0.788 0.044 0.003
## 4 | 18.070 | -12.004 8.544 0.441 | -4.188 1.248 0.054
## 5 | 13.723 | -5.894 2.059 0.184 | 3.357 0.802 0.060
## 6 | 12.703 | -1.170 0.081 0.008 | 5.334 2.025 0.176
## 7 | 13.491 | -4.288 1.090 0.101 | -5.111 1.859 0.144
## 8 | 21.865 | -12.565 9.361 0.330 | -11.661 9.678 0.284
## 9 | 16.181 | -1.640 0.159 0.010 | 6.056 2.610 0.140
## 10 | 15.124 | 0.660 0.026 0.002 | 8.082 4.648 0.286
## Dim.3 ctr cos2
## 1 | -11.016 10.557 0.288 |
## 2 | -4.630 1.865 0.082 |
## 3 | -1.115 0.108 0.006 |
## 4 | -3.346 0.974 0.034 |
## 5 | -2.145 0.400 0.024 |
## 6 | -1.038 0.094 0.007 |
## 7 | 3.687 1.183 0.075 |
## 8 | 3.611 1.135 0.027 |
## 9 | -5.668 2.795 0.123 |
## 10 | -0.871 0.066 0.003 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 272.9587_0.485 | -0.003 0.000 0.000 | -0.025 0.004 0.020 | 0.011 0.001
## 288.9363_0.485 | 0.017 0.001 0.013 | -0.035 0.007 0.055 | 0.023 0.004
## 226.9658_0.499 | 0.015 0.001 0.021 | -0.010 0.001 0.010 | 0.001 0.000
## 294.9532_0.5 | 0.016 0.001 0.019 | 0.006 0.000 0.003 | 0.006 0.000
## 362.9406_0.501 | 0.015 0.001 0.027 | -0.005 0.000 0.004 | -0.001 0.000
## 520.9099_0.514 | 0.003 0.000 0.002 | 0.018 0.002 0.059 | -0.016 0.002
## 588.8973_0.514 | -0.005 0.000 0.005 | 0.025 0.004 0.110 | -0.009 0.001
## 452.9225_0.515 | -0.001 0.000 0.000 | 0.015 0.001 0.064 | -0.011 0.001
## 656.8848_0.515 | 0.012 0.001 0.014 | 0.029 0.005 0.082 | -0.022 0.004
## 724.8722_0.515 | -0.003 0.000 0.001 | 0.026 0.004 0.069 | -0.016 0.002
## cos2
## 272.9587_0.485 0.004 |
## 288.9363_0.485 0.024 |
## 226.9658_0.499 0.000 |
## 294.9532_0.5 0.003 |
## 362.9406_0.501 0.000 |
## 520.9099_0.514 0.044 |
## 588.8973_0.514 0.014 |
## 452.9225_0.515 0.036 |
## 656.8848_0.515 0.048 |
## 724.8722_0.515 0.028 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 5101 | 10.444 | -1.428 0.019 -0.456 | 2.925 0.078 1.024 |
## 5102 | 10.618 | -2.638 0.062 -0.842 | -1.669 0.025 -0.584 |
## 5103 | 12.500 | -5.469 0.191 -1.747 | -2.939 0.055 -1.028 |
## 5104 | 11.850 | -6.483 0.299 -2.071 | -5.359 0.205 -1.875 |
## 5105 | 13.414 | -4.523 0.114 -1.445 | -5.311 0.157 -1.858 |
## 5107 | 16.936 | 1.141 0.005 0.364 | 0.730 0.002 0.255 |
## 5108 | 14.400 | -3.240 0.051 -1.035 | -0.927 0.004 -0.324 |
## 5109 | 14.516 | -8.943 0.380 -2.856 | -1.700 0.014 -0.595 |
## 5110 | 12.013 | -4.309 0.129 -1.376 | 2.852 0.056 0.998 |
## 5111 | 12.776 | -1.482 0.013 -0.473 | -2.394 0.035 -0.838 |
## Dim.3 cos2 v.test
## 5101 0.027 0.000 0.010 |
## 5102 4.581 0.186 1.772 |
## 5103 0.366 0.001 0.142 |
## 5104 1.728 0.021 0.669 |
## 5105 -0.270 0.000 -0.104 |
## 5107 -7.823 0.213 -3.026 |
## 5108 -6.267 0.189 -2.425 |
## 5109 -2.230 0.024 -0.863 |
## 5110 -3.463 0.083 -1.340 |
## 5111 7.980 0.390 3.087 |
| Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5101 | | | 10.444 | | | -1.428 | 0.019 | -0.456 | | | 2.925 | 0.078 | 1.024 | | | 0.027 | 0.000 | 0.010 | | |
| 5102 | | | 10.618 | | | -2.638 | 0.062 | -0.842 | | | -1.669 | 0.025 | -0.584 | | | 4.581 | 0.186 | 1.772 | | |
| 5103 | | | 12.500 | | | -5.469 | 0.191 | -1.747 | | | -2.939 | 0.055 | -1.028 | | | 0.366 | 0.001 | 0.142 | | |
| 5104 | | | 11.850 | | | -6.483 | 0.299 | -2.071 | | | -5.359 | 0.205 | -1.875 | | | 1.728 | 0.021 | 0.669 | | |
| 5105 | | | 13.414 | | | -4.523 | 0.114 | -1.445 | | | -5.311 | 0.157 | -1.858 | | | -0.270 | 0.000 | -0.104 | | |
| 5107 | | | 16.936 | | | 1.141 | 0.005 | 0.364 | | | 0.730 | 0.002 | 0.255 | | | -7.823 | 0.213 | -3.026 | | |
| 5108 | | | 14.400 | | | -3.240 | 0.051 | -1.035 | | | -0.927 | 0.004 | -0.324 | | | -6.267 | 0.189 | -2.425 | | |
| 5109 | | | 14.516 | | | -8.943 | 0.380 | -2.856 | | | -1.700 | 0.014 | -0.595 | | | -2.230 | 0.024 | -0.863 | | |
| 5110 | | | 12.013 | | | -4.309 | 0.129 | -1.376 | | | 2.852 | 0.056 | 0.998 | | | -3.463 | 0.083 | -1.340 | | |
| 5111 | | | 12.776 | | | -1.482 | 0.013 | -0.473 | | | -2.394 | 0.035 | -0.838 | | | 7.980 | 0.390 | 3.087 | | |
# pull PC coordinates into df
PC_coord_QC_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2$ind$coord)
# bind back metadata from cols 1-14
PC_coord_QC_log2 <- bind_cols(DC_imp_metabind_clust_log2[,1:14], PC_coord_QC_log2)
# grab some variance explained
importance_QC <- PCA.DC_imp_metabind_clust_log2$eig
# set variance explained with PC1, round to 2 digits
PC1_withQC <- round(importance_QC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_withQC <- round(importance_QC[2,2], 2)Using FactoExtra package
| eigenvalue | variance.percent | cumulative.variance.percent | |
|---|---|---|---|
| Dim.1 | 19.8421755 | 11.4518536 | 11.45185 |
| Dim.2 | 16.5307904 | 9.5406974 | 20.99255 |
| Dim.3 | 13.5237358 | 7.8051846 | 28.79774 |
| Dim.4 | 12.3020654 | 7.1001011 | 35.89784 |
| Dim.5 | 11.0647299 | 6.3859765 | 42.28381 |
| Dim.6 | 8.4040513 | 4.8503736 | 47.13419 |
| Dim.7 | 7.4956565 | 4.3260962 | 51.46028 |
| Dim.8 | 6.8924751 | 3.9779718 | 55.43825 |
| Dim.9 | 6.2447866 | 3.6041603 | 59.04242 |
| Dim.10 | 5.6237895 | 3.2457537 | 62.28817 |
| Dim.11 | 4.8493397 | 2.7987822 | 65.08695 |
| Dim.12 | 4.4587015 | 2.5733265 | 67.66028 |
| Dim.13 | 3.9898243 | 2.3027154 | 69.96299 |
| Dim.14 | 3.6072051 | 2.0818879 | 72.04488 |
| Dim.15 | 3.1145811 | 1.7975714 | 73.84245 |
| Dim.16 | 3.0286837 | 1.7479960 | 75.59045 |
| Dim.17 | 2.8301684 | 1.6334234 | 77.22387 |
| Dim.18 | 2.5587894 | 1.4767978 | 78.70067 |
| Dim.19 | 2.3971540 | 1.3835104 | 80.08418 |
| Dim.20 | 2.1496833 | 1.2406834 | 81.32486 |
| Dim.21 | 2.0008810 | 1.1548026 | 82.47967 |
| Dim.22 | 1.9309956 | 1.1144685 | 83.59413 |
| Dim.23 | 1.8520226 | 1.0688894 | 84.66302 |
| Dim.24 | 1.5674275 | 0.9046362 | 85.56766 |
| Dim.25 | 1.4130659 | 0.8155469 | 86.38321 |
| Dim.26 | 1.3585949 | 0.7841091 | 87.16732 |
| Dim.27 | 1.2472250 | 0.7198322 | 87.88715 |
| Dim.28 | 1.1894317 | 0.6864770 | 88.57363 |
| Dim.29 | 1.1353607 | 0.6552701 | 89.22890 |
| Dim.30 | 1.1028196 | 0.6364891 | 89.86538 |
| Dim.31 | 0.9532155 | 0.5501455 | 90.41553 |
| Dim.32 | 0.9463791 | 0.5461999 | 90.96173 |
| Dim.33 | 0.8528924 | 0.4922444 | 91.45397 |
| Dim.34 | 0.8239744 | 0.4755544 | 91.92953 |
| Dim.35 | 0.7900000 | 0.4559462 | 92.38547 |
| Dim.36 | 0.7269618 | 0.4195639 | 92.80504 |
| Dim.37 | 0.7230376 | 0.4172990 | 93.22234 |
| Dim.38 | 0.6367755 | 0.3675131 | 93.58985 |
| Dim.39 | 0.6181784 | 0.3567799 | 93.94663 |
| Dim.40 | 0.5747396 | 0.3317093 | 94.27834 |
| Dim.41 | 0.5406162 | 0.3120151 | 94.59036 |
| Dim.42 | 0.4989367 | 0.2879599 | 94.87832 |
| Dim.43 | 0.4764383 | 0.2749750 | 95.15329 |
| Dim.44 | 0.4463250 | 0.2575952 | 95.41089 |
| Dim.45 | 0.4281641 | 0.2471136 | 95.65800 |
| Dim.46 | 0.4232934 | 0.2443026 | 95.90230 |
| Dim.47 | 0.3890995 | 0.2245677 | 96.12687 |
| Dim.48 | 0.3818560 | 0.2203871 | 96.34726 |
| Dim.49 | 0.3420712 | 0.1974254 | 96.54468 |
| Dim.50 | 0.3359414 | 0.1938876 | 96.73857 |
| Dim.51 | 0.3224576 | 0.1861055 | 96.92467 |
| Dim.52 | 0.3094896 | 0.1786210 | 97.10330 |
| Dim.53 | 0.2938807 | 0.1696124 | 97.27291 |
| Dim.54 | 0.2771909 | 0.1599799 | 97.43289 |
| Dim.55 | 0.2718670 | 0.1569073 | 97.58980 |
| Dim.56 | 0.2630650 | 0.1518272 | 97.74162 |
| Dim.57 | 0.2493399 | 0.1439058 | 97.88553 |
| Dim.58 | 0.2401879 | 0.1386238 | 98.02415 |
| Dim.59 | 0.2310412 | 0.1333448 | 98.15750 |
| Dim.60 | 0.2193334 | 0.1265876 | 98.28408 |
| Dim.61 | 0.2038445 | 0.1176482 | 98.40173 |
| Dim.62 | 0.1959995 | 0.1131205 | 98.51485 |
| Dim.63 | 0.1915519 | 0.1105536 | 98.62541 |
| Dim.64 | 0.1851354 | 0.1068503 | 98.73226 |
| Dim.65 | 0.1699516 | 0.0980870 | 98.83034 |
| Dim.66 | 0.1619971 | 0.0934962 | 98.92384 |
| Dim.67 | 0.1565721 | 0.0903652 | 99.01421 |
| Dim.68 | 0.1475668 | 0.0851678 | 99.09937 |
| Dim.69 | 0.1468370 | 0.0847466 | 99.18412 |
| Dim.70 | 0.1401048 | 0.0808611 | 99.26498 |
| Dim.71 | 0.1372662 | 0.0792228 | 99.34420 |
| Dim.72 | 0.1255668 | 0.0724705 | 99.41667 |
| Dim.73 | 0.1251464 | 0.0722279 | 99.48890 |
| Dim.74 | 0.1192392 | 0.0688186 | 99.55772 |
| Dim.75 | 0.1089387 | 0.0628736 | 99.62059 |
| Dim.76 | 0.1062115 | 0.0612997 | 99.68189 |
| Dim.77 | 0.0996014 | 0.0574847 | 99.73938 |
| Dim.78 | 0.0917010 | 0.0529249 | 99.79230 |
| Dim.79 | 0.0875116 | 0.0505071 | 99.84281 |
| Dim.80 | 0.0820157 | 0.0473351 | 99.89015 |
| Dim.81 | 0.0798054 | 0.0460594 | 99.93620 |
| Dim.82 | 0.0742665 | 0.0428627 | 99.97907 |
| Dim.83 | 0.0362668 | 0.0209313 | 100.00000 |
| Dim.84 | 0.0000019 | 0.0000011 | 100.00000 |
# manual scores plot
(PCA_withQCs <- PC_coord_QC_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = factor(treatment, levels = c("control", "beta", "red", "QC")),
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("lightgreen", "orange", "tomato", "lightgray")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_withQC/PC1_withQC) +
labs(x = glue::glue("PC1: {PC1_withQC}%"),
y = glue::glue("PC2: {PC2_withQC}%"),
fill = "Group",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data"))DC_imp_metabind_clust_log2_noQCs <- DC_imp_metabind_clust_log2 %>%
filter(treatment != "QC")
PCA.DC_imp_metabind_clust_log2_noQCs <- PCA(DC_imp_metabind_clust_log2_noQCs, # wide data
quali.sup=1:14, # remove qualitative variables
graph=FALSE, # don't graph
scale.unit=FALSE) # don't scale, we already did this
# look at summary
kable(summary(PCA.DC_imp_metabind_clust_log2_noQCs))##
## Call:
## PCA(X = DC_imp_metabind_clust_log2_noQCs, scale.unit = FALSE,
## quali.sup = 1:14, graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 21.617 16.532 14.989 13.437 10.636 9.166 8.369
## % of var. 11.456 8.761 7.943 7.121 5.637 4.858 4.435
## Cumulative % of var. 11.456 20.217 28.161 35.281 40.918 45.776 50.211
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 7.644 7.304 6.151 5.734 5.193 4.398 3.791
## % of var. 4.051 3.871 3.260 3.039 2.752 2.331 2.009
## Cumulative % of var. 54.261 58.132 61.392 64.431 67.183 69.514 71.523
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 3.678 3.434 3.205 3.053 2.653 2.420 2.342
## % of var. 1.949 1.820 1.698 1.618 1.406 1.282 1.241
## Cumulative % of var. 73.472 75.292 76.991 78.609 80.015 81.297 82.539
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 2.245 1.951 1.734 1.649 1.511 1.447 1.378
## % of var. 1.190 1.034 0.919 0.874 0.801 0.767 0.730
## Cumulative % of var. 83.729 84.762 85.681 86.555 87.356 88.122 88.852
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 1.331 1.158 1.147 1.031 0.999 0.960 0.888
## % of var. 0.705 0.614 0.608 0.546 0.530 0.509 0.471
## Cumulative % of var. 89.558 90.172 90.780 91.326 91.855 92.364 92.835
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.880 0.778 0.743 0.695 0.665 0.601 0.584
## % of var. 0.466 0.412 0.394 0.368 0.352 0.318 0.310
## Cumulative % of var. 93.301 93.713 94.107 94.475 94.827 95.146 95.455
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 0.536 0.521 0.517 0.473 0.458 0.430 0.401
## % of var. 0.284 0.276 0.274 0.251 0.242 0.228 0.212
## Cumulative % of var. 95.739 96.015 96.289 96.540 96.782 97.010 97.222
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54 Dim.55 Dim.56
## Variance 0.378 0.376 0.349 0.343 0.329 0.323 0.314
## % of var. 0.200 0.199 0.185 0.182 0.174 0.171 0.167
## Cumulative % of var. 97.422 97.622 97.807 97.989 98.163 98.334 98.501
## Dim.57 Dim.58 Dim.59 Dim.60 Dim.61 Dim.62 Dim.63
## Variance 0.294 0.278 0.261 0.241 0.232 0.230 0.219
## % of var. 0.156 0.148 0.138 0.128 0.123 0.122 0.116
## Cumulative % of var. 98.656 98.804 98.942 99.070 99.193 99.315 99.431
## Dim.64 Dim.65 Dim.66 Dim.67 Dim.68 Dim.69
## Variance 0.199 0.189 0.185 0.173 0.168 0.160
## % of var. 0.106 0.100 0.098 0.092 0.089 0.085
## Cumulative % of var. 99.537 99.637 99.735 99.827 99.915 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 20.626 | -2.350 0.365 0.013 | -10.222 9.030 0.246
## 2 | 16.336 | 5.053 1.687 0.096 | -4.493 1.744 0.076
## 3 | 13.939 | -3.381 0.756 0.059 | -1.995 0.344 0.020
## 4 | 17.543 | -11.161 8.232 0.405 | -3.795 1.245 0.047
## 5 | 13.107 | -1.471 0.143 0.013 | -3.236 0.905 0.061
## 6 | 12.476 | 3.078 0.626 0.061 | -2.187 0.413 0.031
## 7 | 13.498 | -6.406 2.712 0.225 | 3.774 1.231 0.078
## 8 | 21.675 | -16.885 18.840 0.607 | 3.827 1.266 0.031
## 9 | 15.910 | 3.208 0.680 0.041 | -6.731 3.915 0.179
## 10 | 14.902 | 6.384 2.693 0.184 | -2.210 0.422 0.022
## Dim.3 ctr cos2
## 1 | 4.094 1.598 0.039 |
## 2 | 0.460 0.020 0.001 |
## 3 | -4.606 2.022 0.109 |
## 4 | 2.888 0.795 0.027 |
## 5 | 1.140 0.124 0.008 |
## 6 | -4.520 1.948 0.131 |
## 7 | -8.348 6.642 0.383 |
## 8 | -6.209 3.674 0.082 |
## 9 | -1.775 0.300 0.012 |
## 10 | -3.963 1.497 0.071 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 272.9587_0.485 | -0.023 0.002 0.016 | 0.015 0.001 0.007 | -0.026 0.005
## 288.9363_0.485 | -0.015 0.001 0.009 | 0.034 0.007 0.047 | 0.047 0.014
## 226.9658_0.499 | 0.003 0.000 0.001 | 0.005 0.000 0.002 | 0.000 0.000
## 294.9532_0.5 | 0.018 0.001 0.023 | 0.009 0.000 0.005 | 0.019 0.002
## 362.9406_0.501 | 0.005 0.000 0.003 | 0.000 0.000 0.000 | -0.014 0.001
## 520.9099_0.514 | 0.017 0.001 0.045 | -0.019 0.002 0.060 | 0.009 0.001
## 588.8973_0.514 | 0.016 0.001 0.041 | -0.015 0.001 0.034 | -0.018 0.002
## 452.9225_0.515 | 0.012 0.001 0.036 | -0.015 0.001 0.056 | 0.005 0.000
## 656.8848_0.515 | 0.030 0.004 0.076 | -0.029 0.005 0.070 | -0.008 0.000
## 724.8722_0.515 | 0.019 0.002 0.035 | -0.023 0.003 0.053 | -0.024 0.004
## cos2
## 272.9587_0.485 0.021 |
## 288.9363_0.485 0.087 |
## 226.9658_0.499 0.000 |
## 294.9532_0.5 0.027 |
## 362.9406_0.501 0.022 |
## 520.9099_0.514 0.013 |
## 588.8973_0.514 0.047 |
## 452.9225_0.515 0.006 |
## 656.8848_0.515 0.005 |
## 724.8722_0.515 0.054 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 5101 | 10.150 | 1.395 0.019 0.427 | -0.524 0.003 -0.183 |
## 5102 | 10.416 | -2.515 0.058 -0.770 | 4.671 0.201 1.637 |
## 5103 | 12.264 | -5.713 0.217 -1.750 | 0.154 0.000 0.054 |
## 5104 | 11.605 | -8.031 0.479 -2.461 | 1.904 0.027 0.667 |
## 5105 | 13.384 | -6.744 0.254 -2.066 | 0.113 0.000 0.040 |
## 5107 | 17.064 | 1.352 0.006 0.414 | -7.358 0.186 -2.578 |
## 5108 | 14.342 | -2.984 0.043 -0.914 | -6.401 0.199 -2.243 |
## 5109 | 13.984 | -7.271 0.270 -2.228 | -2.895 0.043 -1.014 |
## 5110 | 11.507 | -0.782 0.005 -0.240 | -4.276 0.138 -1.498 |
## 5111 | 12.772 | -2.192 0.029 -0.672 | 8.115 0.404 2.843 |
## Dim.3 cos2 v.test
## 5101 -0.963 0.009 -0.354 |
## 5102 1.398 0.018 0.514 |
## 5103 -4.590 0.140 -1.689 |
## 5104 -1.593 0.019 -0.586 |
## 5105 -3.762 0.079 -1.384 |
## 5107 2.277 0.018 0.838 |
## 5108 -4.617 0.104 -1.699 |
## 5109 -0.859 0.004 -0.316 |
## 5110 -1.166 0.010 -0.429 |
## 5111 -2.251 0.031 -0.828 |
| Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5101 | | | 10.150 | | | 1.395 | 0.019 | 0.427 | | | -0.524 | 0.003 | -0.183 | | | -0.963 | 0.009 | -0.354 | | |
| 5102 | | | 10.416 | | | -2.515 | 0.058 | -0.770 | | | 4.671 | 0.201 | 1.637 | | | 1.398 | 0.018 | 0.514 | | |
| 5103 | | | 12.264 | | | -5.713 | 0.217 | -1.750 | | | 0.154 | 0.000 | 0.054 | | | -4.590 | 0.140 | -1.689 | | |
| 5104 | | | 11.605 | | | -8.031 | 0.479 | -2.461 | | | 1.904 | 0.027 | 0.667 | | | -1.593 | 0.019 | -0.586 | | |
| 5105 | | | 13.384 | | | -6.744 | 0.254 | -2.066 | | | 0.113 | 0.000 | 0.040 | | | -3.762 | 0.079 | -1.384 | | |
| 5107 | | | 17.064 | | | 1.352 | 0.006 | 0.414 | | | -7.358 | 0.186 | -2.578 | | | 2.277 | 0.018 | 0.838 | | |
| 5108 | | | 14.342 | | | -2.984 | 0.043 | -0.914 | | | -6.401 | 0.199 | -2.243 | | | -4.617 | 0.104 | -1.699 | | |
| 5109 | | | 13.984 | | | -7.271 | 0.270 | -2.228 | | | -2.895 | 0.043 | -1.014 | | | -0.859 | 0.004 | -0.316 | | |
| 5110 | | | 11.507 | | | -0.782 | 0.005 | -0.240 | | | -4.276 | 0.138 | -1.498 | | | -1.166 | 0.010 | -0.429 | | |
| 5111 | | | 12.772 | | | -2.192 | 0.029 | -0.672 | | | 8.115 | 0.404 | 2.843 | | | -2.251 | 0.031 | -0.828 | | |
# pull PC coordinates into df
PC_coord_noQCs_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2_noQCs$ind$coord)
# bind back metadata from cols 1-14
PC_coord_noQCs_log2 <- bind_cols(DC_imp_metabind_clust_log2_noQCs[,1:14], PC_coord_noQCs_log2)
# grab some variance explained
importance_noQC <- PCA.DC_imp_metabind_clust_log2_noQCs$eig
# set variance explained with PC1, round to 2 digits
PC1_noQC <- round(importance_noQC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_noQC <- round(importance_noQC[2,2], 2)Using FactoExtra
# wrangling data prior to plot for ease
PC_coord_noQCs_log2 <- PC_coord_noQCs_log2 %>%
mutate(sample2 = sample) %>%
mutate_at("sample2", str_sub, start=7, end=8) %>%
mutate(period = sample2) %>%
unite(treatment_period, "treatment", "period", sep = "_", remove = FALSE) %>%
dplyr::select(!sample2) %>%
mutate_at("period", as.factor) %>%
# relevel factors
mutate(treatment_period = fct_relevel(treatment_period, c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3")),
treatment = fct_relevel(treatment, c("control", "beta", "red")))(PCA_withoutQCs <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan3", "orangered2",
"lavenderblush3", "darkred")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "Group",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs"))(PCA_faceted_noQCs <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan3", "orangered2",
"lavenderblush3", "darkred")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "treatment_period",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ period) +
theme(strip.background = element_rect(fill="white")))PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = sex,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("purple", "blue")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "sex",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ period) +
theme(strip.background = element_rect(fill="white"))trt_labels <- c("control" = "Control",
"beta" = "High \U03b2-Carotene",
"red" = "High Lycopene")
(PCA_faceted_noQCs2 <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre high \U03b2-carotene", "post high \U03b2-carotene",
"pre high lycopene", "post high lycopene")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "Intervention timepoint",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed LC-MS lipidomics data (ESI -) faceted by treatment group") +
facet_wrap( ~ treatment, labeller = as_labeller(trt_labels)))Export plot
ggsave(plot = PCA_faceted_noQCs2,
filename = "plots and figures/PCA-faceted-by-group-lipidomicsNEG.svg",
bg = "transparent",
height = 4,
width = 8)PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = sex,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("purple", "blue")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "sex",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ treatment) +
theme(strip.background = element_rect(fill="white"))(PC_coord_facetsex <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan3", "orangered2",
"lavenderblush3", "darkred")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "sex",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ sex) +
theme(strip.background = element_rect(fill="white")))This type of PCA accounts for the structure of paired data, allowing for a more accurate assessment of biological differences between treatment groups, not differences attributed to the natural variation between individuals.
See http://mixomics.org/methods/multilevel/ for more info.
Data_forMPCA <- DC_imp_metabind_clust_log2_noQCs %>%
mutate_at("subject", as.factor) %>%
mutate(sample2 = sample,
b_c = carotenoids$b_c_nmol_l_plasma,
lyc = carotenoids$lyc_nmol_l_plasma,
apo13one = carotenoids$apo13one_nmol_l_plasma,
retinol = carotenoids$retinol_nmol_l_plasma,
total_carot = carotenoids$total_carotenoids) %>%
mutate_at("sample2", str_sub, start=7, end=8) %>%
mutate(period = sample2) %>%
dplyr::select(c(1:14), period, b_c, lyc, apo13one, retinol, total_carot, everything()) %>%
dplyr::select(!sample2)
summary(as.factor(Data_forMPCA$subject))## 5101 5102 5103 5104 5105 5107 5108 5109 5110 5111 5112 5113 5114 5115 5116 5117
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 5134 5135 5136
## 2 2 2
## [1] "subject" "treatment" "tomato_or_control"
## [4] "sex" "bmi" "age"
## [7] "tot_chol" "ldl_chol" "hdl_chol"
## [10] "triglycerides" "glucose" "SBP"
## [13] "DBP" "sample" "period"
## [16] "b_c" "lyc" "apo13one"
## [19] "retinol" "total_carot"
mixOmicsPCA.result <- mixOmics::pca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
scale = FALSE,
center = FALSE)
plotIndiv(mixOmicsPCA.result,
ind.names = Data_forMPCA$subject,
group = Data_forMPCA$treatment,
legend = TRUE,
legend.title = "Treatment",
title = 'Regular PCA, Lipidomics C18 (-)')With all data
multilevelPCA.result <- mixOmics::pca(Data_forMPCA[,-(c(1:20))],
multilevel = Data_forMPCA$subject,
scale = FALSE,
center = FALSE,ncomp = 10)
plotIndiv(multilevelPCA.result,
ind.names = Data_forMPCA$period,
group = Data_forMPCA$treatment,
legend = TRUE,
legend.title = "Treatment",
title = 'Multilevel PCA, Lipidomics C18 (-)',comp = c(1,2))Following http://mixomics.org/case-studies/splsda-srbct-case-study/
mat_Data_forMPCA <- Data_forMPCA %>%
dplyr::select(!metavar) %>%
as.matrix()
class_forMPCA <- Data_forMPCA$treatment %>%
as.factor()
# checking dimensions of feature abundance matrix
dim(mat_Data_forMPCA)
# checking the distribution of treatment group
summary(class_forMPCA)“A PLS-DA model is fitted with ten components to evaluate the performance and the number of components necessary for the final model.”
splsda <- splsda(mat_Data_forMPCA, class_forMPCA, ncomp = 3) # set ncomp to 10 for performance assessment later# plot the samples projected onto the first two components of the PLS-DA subspace
plotIndiv(splsda , comp = 1:2,
group = class_forMPCA, ind.names = FALSE, # colour points by class
ellipse = TRUE, # include 95% confidence ellipse for each class
legend = TRUE, title = '(a) PLSDA with confidence ellipses')
# use the max.dist measure to form decision boundaries between classes based on PLS-DA data
background = background.predict(splsda, comp.predicted=2, dist = "max.dist")
# plot the samples projected onto the first two components of the PLS-DA subspace
plotIndiv(splsda, comp = 1:2,
group = class_forMPCA, ind.names = FALSE, # colour points by class
background = background, # include prediction background for each class
legend = TRUE, title = " (b) PLSDA with prediction background")# undergo performance evaluation in order to tune the number of components to use
perf.splsda <- perf(splsda, validation = "loo",
folds = 5, nrepeat = 100, # use repeated cross-validation
progressBar = FALSE, auc = TRUE) # include AUC values
# plot the outcome of performance evaluation across all ten components
plot(perf.splsda, col = color.mixo(5:7), sd = TRUE,
legend.position = "horizontal")
perf.splsda$choice.ncomp # what is the optimal value of components according to perf()# grid of possible keepX values that will be tested for each component
list.keepX <- c(1:10, seq(20, 300, 10))
# undergo the tuning process to determine the optimal number of variables
tune.splsda <- tune.splsda(mat_Data_forMPCA, class_forMPCA, ncomp = 9, # calculate for first 9 components
validation = 'loo',
folds = 5, nrepeat = 100, # use repeated cross-validation
dist = 'max.dist', # use max.dist measure
measure = "BER", # use balanced error rate of dist measure
test.keepX = list.keepX,
cpus = 2) # allow for parallelization to decrease runtime
plot(tune.splsda, col = color.jet(9)) # plot output of variable number tuningresult.sPCA.multi <- spca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
keepX = c(10, 10),
multilevel = Data_forMPCA$subject)
plotIndiv(result.sPCA.multi,
ind.names = Data_forMPCA$period,
group = Data_forMPCA$treatment,
legend = TRUE,
legend.title = "Treatment",
title = 'Multilevel sPCA, Lipidomics C18 (-)',comp = c(1,2))## [1] "855.6591_9.033" "857.6749_9.986" "829.6431_8.816" "925.6617_9.988"
## [5] "843.6586_9.396" "827.6272_7.905" "923.646_9.035" "819.6141_8.816"
## [9] "849.6462_9.988" "909.6368_9.99"
# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_forPCAtools <- Data_forMPCA %>%
# select only sample ID and feature columns
dplyr::select(sample,
21:ncol(.)) %>%
# transpose
t() %>%
# convert back to df
as.data.frame()
names(imp_clust_omicsdata_forPCAtools) <- imp_clust_omicsdata_forPCAtools[1,] # make samp;e IDs column names
imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools[-1,] # remove sample ID row
# create metadata df suitable for PCAtools pckg
metadata_forPCAtools <- Data_forMPCA[,1:20]
metadata_forPCAtools <- metadata_forPCAtools %>%
unite("treatment_period", c("treatment", "period"), sep = "_", remove = FALSE) %>%
column_to_rownames("sample")
# create a vector so that col names in abundance df matches metadata df
order_forPCAtools <- match(colnames(imp_clust_omicsdata_forPCAtools), rownames(metadata_forPCAtools))
# reorder col names in abundance df so that it matches metadata
log2_abundances_reordered_forPCAtools <- imp_clust_omicsdata_forPCAtools[,order_forPCAtools] %>%
# change abundance df to numeric
mutate_all(as.numeric)# pca
p <- PCAtools::pca(log2_abundances_reordered_forPCAtools,
metadata = metadata_forPCAtools,
scale = FALSE, # using scaled data already (log2 transformed)
)
biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'treatment',
colkey = c("control" = "lightgreen",
"beta" = "orange",
"red" = "tomato"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE,ntopLoadings = 10)# pca
biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'period',
colkey = c("b1" = "gray",
"b3" = "pink"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE)biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'treatment_period',
colkey = c("control_b1" = "darkseagreen2",
"control_b3" = "darkgreen",
"beta_b1" = "tan3",
"beta_b3" = "orangered2",
"red_b1" = "lavenderblush3",
"red_b3" = "darkred"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses.",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE, ntopLoadings = 10)biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'treatment_period',
colkey = c("control_b1" = "darkseagreen2",
"control_b3" = "darkgreen",
"beta_b1" = "tan3",
"beta_b3" = "orangered2",
"red_b1" = "lavenderblush3",
"red_b3" = "darkred"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses.",
showLoadings = TRUE, ntopLoadings = 10)Let’s explore a little more
How many PCs do we need to capture at least 80% variance?
## PC19
## 19
This shows we’d need quite a few PCs to capture most of the variance.
Here, we will look at separations for several components at once using pairs plots.
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'treatment_period',
colkey = c("control_b1" = "darkseagreen2",
"control_b3" = "darkgreen",
"beta_b1" = "tan",
"beta_b3" = "orangered2",
"red_b1" = "lavenderblush3",
"red_b3" = "darkred"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'period',
colkey = c("b1" = "darkgray",
"b3" = "pink"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))Are there any obvious clusterings when colored by sex?
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'sex',
colkey = c("M" = "red",
"F" = "purple"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))This is a cool way to explore the correlations between the metadata and the PCs! I want to look at how the metavariables correlate with PCs that account for 80% variation in the dataset.
Again: How many PCs do we need to capture at least 80% variance?
## PC19
## 19
eigencorplot(p,
components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]), # get components that account for 80% variance
metavars = colnames(metadata_forPCAtools),
col = c('darkblue', 'blue2', 'gray', 'red2', 'darkred'),
cexCorval = 0.7,
colCorval = 'white',
fontCorval = 2,
posLab = 'bottomleft',
rotLabX = 45,
posColKey = 'top',
cexLabColKey = 1.5,
scale = TRUE,
main = 'PC1-15 metadata correlations',
colFrame = 'white',
plotRsquared = FALSE) eigencorplot(p,
components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]),
metavars = colnames(metadata_forPCAtools),
col = c('white', 'cornsilk1', 'gold', 'forestgreen', 'darkgreen'),
cexCorval = 1.2,
fontCorval = 2,
posLab = 'all',
rotLabX = 45,
scale = TRUE,
main = bquote(Principal ~ component ~ Spearman ~ r^2 ~ metadata ~ correlates),
plotRsquared = TRUE,
corFUN = 'spearman',
corUSE = 'pairwise.complete.obs',
corMultipleTestCorrection = 'BH',
signifSymbols = c('****', '***', '**', '*', ''),
signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1))df_for_stats$treatment_period <- as.factor(df_for_stats$treatment_period)
trt_anova_output_df <- df_for_stats %>%
dplyr::select(subject, sample, treatment_period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
anova_test(rel_abund_log2 ~ treatment_period, wid = subject,
detailed = TRUE) %>%
adjust_pvalue(method = "BH") %>%
as.data.frame()
trt_anova_sig <- trt_anova_output_df %>%
filter(p.adj < .05)
head(trt_anova_sig)## mz_rt Effect SSn SSd DFn DFd F p p<.05
## 1 1334.2417_10.855 treatment_period 5.305 14.827 5 64 4.579 0.001000 *
## 2 1335.245_10.854 treatment_period 4.803 11.811 5 64 5.205 0.000453 *
## 3 1554.1226_7.773 treatment_period 1.342 3.604 5 64 4.766 0.000916 *
## 4 1561.2028_9.038 treatment_period 3.484 10.028 5 64 4.447 0.002000 *
## 5 201.0226_0.688 treatment_period 31.378 91.809 5 64 4.375 0.002000 *
## 6 244.908_0.706 treatment_period 4.986 13.922 5 64 4.585 0.001000 *
## ges p.adj
## 1 0.263 0.02890909
## 2 0.289 0.02274537
## 3 0.271 0.02890909
## 4 0.258 0.04892308
## 5 0.255 0.04892308
## 6 0.264 0.02890909
## [1] 39
# run paired t-tests for control intervention
ctrl_t.test_paired <- df_for_stats %>%
filter(treatment == "control") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
ctrl_t.test_paired_sig <- ctrl_t.test_paired %>%
filter(p < 0.05)
kable(ctrl_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1008.5435_7.813 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.398326 | 10 | 0.0374000 | * |
| 1061.6776_3.089 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.649027 | 10 | 0.0244000 | * |
| 1235.798_10.088 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.718321 | 10 | 0.0216000 | * |
| 1279.8241_10.52 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.889474 | 10 | 0.0030100 | ** |
| 1334.2417_10.855 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.970460 | 10 | 0.0026400 | ** |
| 1335.245_10.854 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.756644 | 10 | 0.0007720 | *** |
| 1394.0685_5.684 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.994221 | 10 | 0.0135000 | * |
| 151.0261_0.612 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.352314 | 10 | 0.0073400 | ** |
| 1554.1226_7.773 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.268520 | 10 | 0.0003640 | *** |
| 1555.1258_7.773 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.041887 | 10 | 0.0005050 | *** |
| 1598.1714_7.758 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.278484 | 10 | 0.0083100 | ** |
| 1600.0951_6.94 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.493591 | 10 | 0.0318000 | * |
| 1605.0902_6.358 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.905978 | 10 | 0.0157000 | * |
| 1616.1136_7.768 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.966338 | 10 | 0.0141000 | * |
| 1632.139_7.776 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.619990 | 10 | 0.0046900 | ** |
| 1633.1422_7.775 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.370406 | 10 | 0.0014000 | ** |
| 1637.3241_10.442 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.441127 | 10 | 0.0348000 | * |
| 1646.3475_10.439 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.320767 | 10 | 0.0015100 | ** |
| 1654.1535_8.02 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.344590 | 10 | 0.0410000 | * |
| 1655.1569_8.02 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.128094 | 10 | 0.0107000 | * |
| 1664.3521_10.59 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.587703 | 10 | 0.0049500 | ** |
| 1665.3553_10.589 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.222488 | 10 | 0.0017600 | ** |
| 1666.3553_10.589 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.570495 | 10 | 0.0050900 | ** |
| 167.021_0.602 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.367841 | 10 | 0.0071500 | ** |
| 1674.3803_10.59 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.733095 | 10 | 0.0038900 | ** |
| 1675.384_10.589 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.328586 | 10 | 0.0076400 | ** |
| 188.9862_0.644 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.406827 | 10 | 0.0013200 | ** |
| 201.0226_0.688 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.352607 | 10 | 0.0405000 | * |
| 203.0013_0.66 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.574806 | 10 | 0.0277000 | * |
| 213.0223_0.689 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.470479 | 10 | 0.0012000 | ** |
| 239.0923_0.975 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.481271 | 10 | 0.0325000 | * |
| 288.9363_0.485 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.738739 | 10 | 0.0209000 | * |
| 291.0839_0.618 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.850671 | 10 | 0.0172000 | * |
| 291.0953_0.67 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.203689 | 10 | 0.0094300 | ** |
| 311.2224_2.465 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.346546 | 10 | 0.0409000 | * |
| 352.0856_0.687 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.402798 | 10 | 0.0371000 | * |
| 362.9406_0.501 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.757099 | 10 | 0.0202000 | * |
| 383.1529_0.667 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.613511 | 10 | 0.0259000 | * |
| 391.2849_2.246 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.636218 | 10 | 0.0045700 | ** |
| 397.3681_5 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.511253 | 10 | 0.0002580 | *** |
| 411.3837_5.349 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.115092 | 10 | 0.0020900 | ** |
| 437.0542_0.688 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.544781 | 10 | 0.0010700 | ** |
| 441.3942_4.718 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.009725 | 10 | 0.0001300 | *** |
| 462.1763_0.655 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.989895 | 10 | 0.0136000 | * |
| 506.3245_3.232 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.988165 | 10 | 0.0136000 | * |
| 507.223_1.7 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.463711 | 10 | 0.0000722 | **** |
| 507.223_2.273 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.077456 | 10 | 0.0004800 | *** |
| 524.3351_3.376 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.404611 | 10 | 0.0067200 | ** |
| 528.2631_0.691 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.343265 | 10 | 0.0411000 | * |
| 539.4309_4.416 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.944283 | 10 | 0.0147000 | * |
| 551.3582_4.841 | rel_abund_log2 | b1 | b3 | 11 | 11 | -8.525686 | 10 | 0.0000067 | **** |
| 557.4566_6.309 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.667967 | 10 | 0.0236000 | * |
| 557.457_6.865 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.417936 | 10 | 0.0362000 | * |
| 559.4719_6.867 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.441306 | 10 | 0.0063200 | ** |
| 566.3462_3.232 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.581398 | 10 | 0.0274000 | * |
| 567.4621_4.643 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.876930 | 10 | 0.0000431 | **** |
| 573.4515_4.266 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.947276 | 10 | 0.0027400 | ** |
| 573.4515_4.873 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.352232 | 10 | 0.0014400 | ** |
| 575.4672_5.354 | rel_abund_log2 | b1 | b3 | 11 | 11 | 8.914095 | 10 | 0.0000045 | **** |
| 575.467_4.662 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.239741 | 10 | 0.0017200 | ** |
| 577.3738_4.918 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.880566 | 10 | 0.0000429 | **** |
| 579.3893_5.557 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.855048 | 10 | 0.0001610 | *** |
| 583.1735_0.618 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.363323 | 10 | 0.0397000 | * |
| 586.3143_2.221 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.599947 | 10 | 0.0265000 | * |
| 591.3894_5.174 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.921007 | 10 | 0.0028600 | ** |
| 591.462_4.314 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.839268 | 10 | 0.0006820 | *** |
| 591.462_4.651 | rel_abund_log2 | b1 | b3 | 11 | 11 | 8.888686 | 10 | 0.0000046 | **** |
| 593.4778_4.664 | rel_abund_log2 | b1 | b3 | 11 | 11 | 9.251220 | 10 | 0.0000032 | **** |
| 595.4933_4.953 | rel_abund_log2 | b1 | b3 | 11 | 11 | 20.304358 | 10 | 0.0000000 | **** |
| 605.4051_5.422 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.490637 | 10 | 0.0320000 | * |
| 618.308_3.232 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.650333 | 10 | 0.0243000 | * |
| 627.1374_0.611 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.157237 | 10 | 0.0019600 | ** |
| 628.5437_10.491 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.029686 | 10 | 0.0024000 | ** |
| 634.3329_3.232 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.443726 | 10 | 0.0062900 | ** |
| 654.5589_10.54 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.657957 | 10 | 0.0240000 | * |
| 656.5586_10.54 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.064140 | 10 | 0.0120000 | * |
| 656.575_10.694 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.607274 | 10 | 0.0047900 | ** |
| 658.0669_0.628 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.624871 | 10 | 0.0254000 | * |
| 676.6243_10.731 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.512566 | 10 | 0.0308000 | * |
| 684.6065_10.853 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.303188 | 10 | 0.0015500 | ** |
| 686.6207_10.914 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.313184 | 10 | 0.0433000 | * |
| 696.6064_10.749 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.245948 | 10 | 0.0485000 | * |
| 711.6251_10.854 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.668546 | 10 | 0.0043300 | ** |
| 716.5225_8.139 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.611030 | 10 | 0.0047600 | ** |
| 723.5203_6.092 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.259499 | 10 | 0.0085800 | ** |
| 733.5494_6.091 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.753136 | 10 | 0.0204000 | * |
| 738.507_7.269 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.284240 | 10 | 0.0455000 | * |
| 746.5966_10.853 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.830675 | 10 | 0.0033200 | ** |
| 752.5589_9.222 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.627420 | 10 | 0.0253000 | * |
| 759.5646_6.287 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.542591 | 10 | 0.0292000 | * |
| 761.5803_7.053 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.748263 | 10 | 0.0037900 | ** |
| 762.5643_8.206 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.825006 | 10 | 0.0180000 | * |
| 762.6216_10.854 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.082883 | 10 | 0.0116000 | * |
| 767.5664_7.562 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.147683 | 10 | 0.0019900 | ** |
| 771.5646_6.096 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.694516 | 10 | 0.0008490 | *** |
| 773.5806_6.749 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.764133 | 10 | 0.0200000 | * |
| 775.5965_7.563 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.234278 | 10 | 0.0089600 | ** |
| 780.4705_7.867 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.657908 | 10 | 0.0240000 | * |
| 789.6114_7.911 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.243948 | 10 | 0.0487000 | * |
| 789.6116_8.177 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.164381 | 10 | 0.0101000 | * |
| 794.5464_7.772 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.705066 | 10 | 0.0008350 | *** |
| 796.5458_7.771 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.727941 | 10 | 0.0008070 | *** |
| 801.6117_7.833 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.452285 | 10 | 0.0341000 | * |
| 803.6275_8.771 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.919414 | 10 | 0.0153000 | * |
| 804.5762_7.774 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.108546 | 10 | 0.0004580 | *** |
| 806.4942_7.271 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.820188 | 10 | 0.0007020 | *** |
| 810.5642_6.968 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.602927 | 10 | 0.0264000 | * |
| 810.5647_7.467 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.368391 | 10 | 0.0071400 | ** |
| 812.5436_6.468 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.338344 | 10 | 0.0415000 | * |
| 815.627_8.417 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.884629 | 10 | 0.0163000 | * |
| 818.6271_10.414 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.481994 | 10 | 0.0011800 | ** |
| 819.6141_8.816 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.278900 | 10 | 0.0459000 | * |
| 819.6141_9.024 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.243826 | 10 | 0.0088100 | ** |
| 820.5694_6.598 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.232154 | 10 | 0.0497000 | * |
| 820.627_10.414 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.813365 | 10 | 0.0007090 | *** |
| 821.565_7.768 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.640792 | 10 | 0.0045300 | ** |
| 821.6139_9.026 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.184462 | 10 | 0.0018700 | ** |
| 821.6152_8.817 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.428282 | 10 | 0.0064600 | ** |
| 822.5774_8.941 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.295648 | 10 | 0.0080700 | ** |
| 823.6294_9.979 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.039762 | 10 | 0.0023600 | ** |
| 824.5441_6.359 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.355183 | 10 | 0.0403000 | * |
| 824.5789_7.841 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.439056 | 10 | 0.0349000 | * |
| 827.6272_7.905 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.564604 | 10 | 0.0010300 | ** |
| 828.6559_10.414 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.789411 | 10 | 0.0035500 | ** |
| 829.6431_8.816 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.933059 | 10 | 0.0150000 | * |
| 829.6432_9.024 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.768161 | 10 | 0.0007590 | *** |
| 831.6576_9.28 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.923286 | 10 | 0.0152000 | * |
| 831.659_9.98 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.935112 | 10 | 0.0028000 | ** |
| 832.6067_8.94 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.917804 | 10 | 0.0154000 | * |
| 832.6428_10.551 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.324458 | 10 | 0.0076900 | ** |
| 833.5544_7.557 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.394152 | 10 | 0.0377000 | * |
| 833.6294_9.395 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.949641 | 10 | 0.0027300 | ** |
| 833.6297_9.665 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.629255 | 10 | 0.0046200 | ** |
| 834.5643_7.242 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.823948 | 10 | 0.0180000 | * |
| 835.5322_8.336 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.124019 | 10 | 0.0108000 | * |
| 835.6457_10.444 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.576877 | 10 | 0.0276000 | * |
| 836.5434_6.295 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.726491 | 10 | 0.0213000 | * |
| 837.6571_10.552 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.234364 | 10 | 0.0089500 | ** |
| 838.5955_8.279 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.469919 | 10 | 0.0331000 | * |
| 840.5749_7.321 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.468933 | 10 | 0.0332000 | * |
| 840.5749_7.471 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.126034 | 10 | 0.0108000 | * |
| 841.5661_6.75 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.215558 | 10 | 0.0003920 | *** |
| 841.6406_8.452 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.891593 | 10 | 0.0030000 | ** |
| 842.6715_10.551 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.432362 | 10 | 0.0002880 | *** |
| 843.6586_9.396 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.554469 | 10 | 0.0052300 | ** |
| 843.6587_9.666 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.167152 | 10 | 0.0019300 | ** |
| 845.6746_10.439 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.415855 | 10 | 0.0363000 | * |
| 846.6588_10.643 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.369900 | 10 | 0.0393000 | * |
| 847.6304_9.032 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.401890 | 10 | 0.0372000 | * |
| 848.5433_6.017 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.783976 | 10 | 0.0193000 | * |
| 849.6458_10.294 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.160454 | 10 | 0.0102000 | * |
| 849.6616_10.589 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.527135 | 10 | 0.0002520 | *** |
| 851.6615_10.59 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.179822 | 10 | 0.0098200 | ** |
| 853.6428_8.12 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.100457 | 10 | 0.0112000 | * |
| 854.5918_8.021 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.629941 | 10 | 0.0046100 | ** |
| 854.6714_10.418 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.280952 | 10 | 0.0457000 | * |
| 855.6591_9.033 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.253318 | 10 | 0.0086700 | ** |
| 856.5377_7.769 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.062711 | 10 | 0.0120000 | * |
| 857.5409_7.768 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.862867 | 10 | 0.0169000 | * |
| 857.6737_9.513 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.964069 | 10 | 0.0026700 | ** |
| 857.6746_10.294 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.642620 | 10 | 0.0246000 | * |
| 859.6907_10.59 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.237116 | 10 | 0.0017200 | ** |
| 860.6369_10.19 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.260431 | 10 | 0.0085700 | ** |
| 860.6919_10.384 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.061764 | 10 | 0.0120000 | * |
| 862.5261_7.47 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.306507 | 10 | 0.0438000 | * |
| 862.527_7.471 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.520338 | 10 | 0.0055400 | ** |
| 862.6535_10.583 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.252514 | 10 | 0.0480000 | * |
| 863.6765_10.683 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.154368 | 10 | 0.0103000 | * |
| 864.6108_7.878 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.406403 | 10 | 0.0369000 | * |
| 865.6758_10.678 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.242872 | 10 | 0.0488000 | * |
| 869.598_7.834 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.578977 | 10 | 0.0275000 | * |
| 871.5502_7.054 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.674593 | 10 | 0.0233000 | * |
| 871.6891_10.337 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.911534 | 10 | 0.0029100 | ** |
| 872.5626_7.769 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.274071 | 10 | 0.0003610 | *** |
| 872.5955_8.863 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.090235 | 10 | 0.0114000 | * |
| 872.6815_10.583 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.914023 | 10 | 0.0006100 | *** |
| 874.6643_9.99 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.660490 | 10 | 0.0239000 | * |
| 876.5748_6.891 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.067863 | 10 | 0.0119000 | * |
| 878.584_5.681 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.468788 | 10 | 0.0332000 | * |
| 878.5901_7.193 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.513654 | 10 | 0.0307000 | * |
| 878.5903_7.305 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.308136 | 10 | 0.0000882 | **** |
| 878.5909_7.805 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.921379 | 10 | 0.0153000 | * |
| 881.6055_9.023 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.636516 | 10 | 0.0045600 | ** |
| 882.6209_8.855 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.424221 | 10 | 0.0358000 | * |
| 883.5331_7.753 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.792130 | 10 | 0.0007320 | *** |
| 883.6211_9.976 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.199866 | 10 | 0.0018300 | ** |
| 884.5687_8.94 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.422232 | 10 | 0.0359000 | * |
| 887.599_8.816 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.514231 | 10 | 0.0307000 | * |
| 890.5493_8.144 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.781392 | 10 | 0.0194000 | * |
| 895.6025_10.452 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.027163 | 10 | 0.0127000 | * |
| 895.6211_9.665 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.511628 | 10 | 0.0308000 | * |
| 897.6305_9.024 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.061240 | 10 | 0.0022800 | ** |
| 903.5878_9.033 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.266245 | 10 | 0.0469000 | * |
| 911.6459_9.666 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.719156 | 10 | 0.0008170 | *** |
| 911.6461_9.397 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.955662 | 10 | 0.0027100 | ** |
| 913.6162_9.035 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.708838 | 10 | 0.0220000 | * |
| 913.6613_10.438 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.360605 | 10 | 0.0399000 | * |
| 917.6482_10.589 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.814375 | 10 | 0.0007080 | *** |
| 918.5543_7.768 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.677322 | 10 | 0.0042700 | ** |
| 918.5546_7.77 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.998605 | 10 | 0.0134000 | * |
| 919.6483_10.59 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.885889 | 10 | 0.0162000 | * |
| 920.6735_10.31 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.455495 | 10 | 0.0002790 | *** |
| 922.5774_7.695 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.624454 | 10 | 0.0254000 | * |
| 923.646_9.035 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.404971 | 10 | 0.0370000 | * |
| 923.6524_10.353 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.080048 | 10 | 0.0116000 | * |
| 924.518_7.766 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.080322 | 10 | 0.0022100 | ** |
| 925.6617_9.988 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.821525 | 10 | 0.0181000 | * |
| 927.677_10.59 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.936365 | 10 | 0.0149000 | * |
| 930.5504_7.815 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.212695 | 10 | 0.0092900 | ** |
| 931.5514_9.582 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.553351 | 10 | 0.0287000 | * |
| 931.6162_9.992 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.624295 | 10 | 0.0254000 | * |
| 931.6162_9.999 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.662141 | 10 | 0.0238000 | * |
| 933.5855_8.781 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.952301 | 10 | 0.0005770 | *** |
| 934.5326_7.772 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.165492 | 10 | 0.0101000 | * |
| 939.6769_10.335 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.036580 | 10 | 0.0125000 | * |
| 943.5073_8.524 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.505690 | 10 | 0.0311000 | * |
| 959.6018_9.026 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.797465 | 10 | 0.0189000 | * |
| 962.5631_8.939 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.259713 | 10 | 0.0474000 | * |
| 966.5651_8.147 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.354157 | 10 | 0.0404000 | * |
| 968.5707_8.019 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.221800 | 10 | 0.0017700 | ** |
| 971.6542_9.988 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.726955 | 10 | 0.0213000 | * |
| 985.616_9.034 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.262055 | 10 | 0.0472000 | * |
## [1] 222
Keep sig features in t-test that have a match in sig ANOVA
paired_ctrl_for_mummichog <- ctrl_t.test_paired %>%
dplyr::select(mz_rt,
p,
statistic) %>%
separate(col = mz_rt,
into = c("m/z", "rt"),
sep = "_") %>%
rename("p-value" = "p") %>%
rename("t-score" = "statistic")
write_csv(paired_ctrl_for_mummichog,
"for mummichog analysis/t-test-res-ctrl-paired.csv")# run paired t-tests for control intervention
beta_t.test_paired <- df_for_stats %>%
filter(treatment == "beta") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
beta_t.test_paired_sig <- beta_t.test_paired %>%
filter(p < 0.05)
kable(beta_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 117.0557_0.669 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.934820 | 11 | 0.0136000 | * |
| 1235.798_10.088 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.381989 | 11 | 0.0061200 | ** |
| 1249.8137_10.468 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.783987 | 11 | 0.0178000 | * |
| 1279.8241_10.52 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.378153 | 11 | 0.0061600 | ** |
| 128.0352_0.617 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.332737 | 11 | 0.0397000 | * |
| 1334.2417_10.855 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.566275 | 11 | 0.0262000 | * |
| 1335.245_10.854 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.456240 | 11 | 0.0053700 | ** |
| 1491.1091_6.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.972539 | 11 | 0.0127000 | * |
| 1554.1226_7.773 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.358112 | 11 | 0.0379000 | * |
| 1558.1192_8.016 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.811807 | 11 | 0.0169000 | * |
| 1561.2028_9.038 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.619617 | 11 | 0.0040300 | ** |
| 1598.0913_6.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.255567 | 11 | 0.0454000 | * |
| 1599.0952_6.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.283561 | 11 | 0.0433000 | * |
| 1602.1228_7.339 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.546669 | 11 | 0.0272000 | * |
| 1616.1136_7.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.355819 | 11 | 0.0381000 | * |
| 1633.1422_7.775 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.484757 | 11 | 0.0051100 | ** |
| 1637.3241_10.442 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.336032 | 11 | 0.0066400 | ** |
| 1641.1859_8.06 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.299035 | 11 | 0.0421000 | * |
| 1646.3475_10.439 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.612640 | 11 | 0.0241000 | * |
| 1655.1569_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.262519 | 11 | 0.0449000 | * |
| 1664.3521_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.629224 | 11 | 0.0039600 | ** |
| 1665.3553_10.589 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.563350 | 11 | 0.0044500 | ** |
| 1669.2155_8.46 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.495141 | 11 | 0.0298000 | * |
| 1670.349_9.987 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.643694 | 11 | 0.0228000 | * |
| 1674.3803_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.221093 | 11 | 0.0081400 | ** |
| 1675.384_10.589 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.354842 | 11 | 0.0064200 | ** |
| 172.9913_0.642 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.157009 | 11 | 0.0091300 | ** |
| 188.9862_0.644 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.262414 | 11 | 0.0075700 | ** |
| 201.0226_0.688 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.701885 | 11 | 0.0206000 | * |
| 201.0575_0.644 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.444664 | 11 | 0.0326000 | * |
| 229.0537_0.703 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.809324 | 11 | 0.0170000 | * |
| 230.9967_0.644 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.087077 | 11 | 0.0103000 | * |
| 291.0839_0.618 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.240244 | 11 | 0.0467000 | * |
| 352.0856_0.687 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.295763 | 11 | 0.0012600 | ** |
| 362.9406_0.501 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.729799 | 11 | 0.0196000 | * |
| 397.2049_0.693 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.627017 | 11 | 0.0235000 | * |
| 397.3681_5 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.147661 | 11 | 0.0016200 | ** |
| 411.3837_5.349 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.602477 | 11 | 0.0000385 | **** |
| 436.2827_3.522 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.789484 | 11 | 0.0030000 | ** |
| 437.0542_0.688 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.234442 | 11 | 0.0079500 | ** |
| 441.3942_4.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.170438 | 11 | 0.0015600 | ** |
| 462.1763_0.655 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.649381 | 11 | 0.0001490 | *** |
| 464.3013_0.999 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.762717 | 11 | 0.0185000 | * |
| 464.314_4.127 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.220324 | 11 | 0.0483000 | * |
| 467.3735_4.306 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.651755 | 11 | 0.0225000 | * |
| 478.2933_3.354 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.711591 | 11 | 0.0034300 | ** |
| 506.3245_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.381037 | 11 | 0.0364000 | * |
| 507.223_1.7 | rel_abund_log2 | b1 | b3 | 12 | 12 | -9.672208 | 11 | 0.0000010 | **** |
| 507.223_2.273 | rel_abund_log2 | b1 | b3 | 12 | 12 | -7.745145 | 11 | 0.0000089 | **** |
| 511.3997_4.453 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.399569 | 11 | 0.0000508 | **** |
| 514.2836_0.69 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.934805 | 11 | 0.0136000 | * |
| 519.347_6.861 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.627030 | 11 | 0.0235000 | * |
| 524.3351_3.376 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.633968 | 11 | 0.0232000 | * |
| 526.3144_2.683 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.371109 | 11 | 0.0062400 | ** |
| 528.2631_0.691 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.440700 | 11 | 0.0328000 | * |
| 539.4309_4.416 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.407874 | 11 | 0.0347000 | * |
| 546.1884_6.859 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.293405 | 11 | 0.0425000 | * |
| 551.3582_4.841 | rel_abund_log2 | b1 | b3 | 12 | 12 | -9.410498 | 11 | 0.0000014 | **** |
| 554.3456_3.441 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.300291 | 11 | 0.0070700 | ** |
| 558.3148_3.234 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.113373 | 11 | 0.0098700 | ** |
| 559.4719_6.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.712253 | 11 | 0.0006370 | *** |
| 566.3462_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.036030 | 11 | 0.0113000 | * |
| 567.4621_4.643 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.257497 | 11 | 0.0013500 | ** |
| 573.4515_4.266 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.282792 | 11 | 0.0002590 | *** |
| 575.4672_5.354 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.902049 | 11 | 0.0004700 | *** |
| 575.467_4.662 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.473259 | 11 | 0.0001940 | *** |
| 577.3738_4.918 | rel_abund_log2 | b1 | b3 | 12 | 12 | -12.254423 | 11 | 0.0000001 | **** |
| 579.3893_5.557 | rel_abund_log2 | b1 | b3 | 12 | 12 | -10.034969 | 11 | 0.0000007 | **** |
| 591.3894_5.174 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.435694 | 11 | 0.0002050 | *** |
| 591.462_4.314 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.390997 | 11 | 0.0358000 | * |
| 591.462_4.651 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.210972 | 11 | 0.0014600 | ** |
| 593.4778_4.664 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.258091 | 11 | 0.0002690 | *** |
| 594.3768_3.858 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.578956 | 11 | 0.0256000 | * |
| 595.4933_4.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | 13.598942 | 11 | 0.0000000 | **** |
| 605.1555_0.616 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.816816 | 11 | 0.0168000 | * |
| 618.308_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.254115 | 11 | 0.0456000 | * |
| 624.3379_0.689 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.882873 | 11 | 0.0149000 | * |
| 634.3329_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.857638 | 11 | 0.0156000 | * |
| 640.5796_10.729 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.639749 | 11 | 0.0230000 | * |
| 654.5589_10.54 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.259625 | 11 | 0.0451000 | * |
| 656.575_10.694 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.001030 | 11 | 0.0121000 | * |
| 664.5877_10.541 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.033159 | 11 | 0.0114000 | * |
| 668.5747_10.647 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.812232 | 11 | 0.0169000 | * |
| 670.5743_10.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.404081 | 11 | 0.0350000 | * |
| 674.6039_10.834 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.534071 | 11 | 0.0001770 | *** |
| 676.6243_10.731 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.813784 | 11 | 0.0005410 | *** |
| 678.6034_10.645 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.592950 | 11 | 0.0250000 | * |
| 678.6402_10.885 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.832092 | 11 | 0.0027800 | ** |
| 682.6332_10.835 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.893687 | 11 | 0.0025000 | ** |
| 684.6065_10.853 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.331193 | 11 | 0.0067000 | ** |
| 696.3033_3.233 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.674102 | 11 | 0.0216000 | * |
| 696.6064_10.749 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.484943 | 11 | 0.0009240 | *** |
| 698.3189_3.775 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.224906 | 11 | 0.0080900 | ** |
| 698.5121_7.962 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.544746 | 11 | 0.0045900 | ** |
| 698.6108_10.776 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.904987 | 11 | 0.0143000 | * |
| 698.6217_10.912 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.861408 | 11 | 0.0155000 | * |
| 700.6211_10.914 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.433943 | 11 | 0.0055800 | ** |
| 705.5178_5.308 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.494284 | 11 | 0.0050200 | ** |
| 716.5225_8.139 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.878249 | 11 | 0.0150000 | * |
| 717.5179_5.106 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.753869 | 11 | 0.0031900 | ** |
| 721.549_6 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.722297 | 11 | 0.0006270 | *** |
| 722.5123_7.861 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.082196 | 11 | 0.0104000 | * |
| 724.5276_8.3 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.621077 | 11 | 0.0007390 | *** |
| 724.8722_0.515 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.377499 | 11 | 0.0367000 | * |
| 725.6412_10.916 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.422712 | 11 | 0.0338000 | * |
| 726.5434_9.157 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.271749 | 11 | 0.0442000 | * |
| 740.5225_7.515 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.294404 | 11 | 0.0424000 | * |
| 744.5538_9.321 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.235466 | 11 | 0.0014000 | ** |
| 746.5966_10.853 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.712025 | 11 | 0.0006380 | *** |
| 750.5436_9.042 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.482944 | 11 | 0.0051200 | ** |
| 752.5591_9.514 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.844641 | 11 | 0.0159000 | * |
| 759.5646_6.287 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.042100 | 11 | 0.0112000 | * |
| 761.5803_7.053 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.873554 | 11 | 0.0151000 | * |
| 762.5643_8.206 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.651552 | 11 | 0.0225000 | * |
| 762.6216_10.854 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.290455 | 11 | 0.0427000 | * |
| 764.5798_8.343 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.417121 | 11 | 0.0342000 | * |
| 765.5735_6.098 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.476697 | 11 | 0.0308000 | * |
| 771.5646_6.096 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.518160 | 11 | 0.0286000 | * |
| 776.5351_7.576 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.096590 | 11 | 0.0102000 | * |
| 776.5354_7.574 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.943227 | 11 | 0.0134000 | * |
| 777.6118_7.986 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.867323 | 11 | 0.0153000 | * |
| 778.5745_10.284 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.625289 | 11 | 0.0236000 | * |
| 780.4705_7.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.918873 | 11 | 0.0024000 | ** |
| 786.5646_7.575 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.707665 | 11 | 0.0034600 | ** |
| 788.5437_6.549 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.376981 | 11 | 0.0367000 | * |
| 789.6114_7.911 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.829449 | 11 | 0.0028000 | ** |
| 789.6116_8.177 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.565200 | 11 | 0.0044300 | ** |
| 790.4993_7.863 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.524980 | 11 | 0.0047600 | ** |
| 790.5953_8.511 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.029372 | 11 | 0.0115000 | * |
| 792.8596_0.515 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.512410 | 11 | 0.0289000 | * |
| 793.5985_8.765 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.687496 | 11 | 0.0211000 | * |
| 800.5357_7.469 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.465914 | 11 | 0.0052800 | ** |
| 801.5445_6.55 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.868405 | 11 | 0.0153000 | * |
| 803.6275_8.771 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.664602 | 11 | 0.0220000 | * |
| 804.5762_7.774 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.657796 | 11 | 0.0223000 | * |
| 804.6308_8.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.894348 | 11 | 0.0146000 | * |
| 805.6426_9.222 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.310559 | 11 | 0.0413000 | * |
| 806.6057_10.613 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.264047 | 11 | 0.0448000 | * |
| 808.5018_9.051 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.982237 | 11 | 0.0021500 | ** |
| 810.5647_7.467 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.545558 | 11 | 0.0008360 | *** |
| 812.5436_6.468 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.950371 | 11 | 0.0022700 | ** |
| 812.58_7.898 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.993280 | 11 | 0.0021100 | ** |
| 815.627_8.417 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.404808 | 11 | 0.0349000 | * |
| 816.5308_6.915 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.337500 | 11 | 0.0393000 | * |
| 816.5751_7.553 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.587367 | 11 | 0.0253000 | * |
| 818.5462_7.192 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.590115 | 11 | 0.0042400 | ** |
| 818.5462_7.337 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.160285 | 11 | 0.0090700 | ** |
| 818.6271_10.414 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.399015 | 11 | 0.0002170 | *** |
| 819.6141_8.816 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.841126 | 11 | 0.0001120 | *** |
| 819.6141_9.024 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.914524 | 11 | 0.0024200 | ** |
| 820.5155_3.079 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.886397 | 11 | 0.0148000 | * |
| 820.5462_7.34 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.296972 | 11 | 0.0423000 | * |
| 820.5463_7.196 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.257837 | 11 | 0.0453000 | * |
| 820.627_10.414 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.933180 | 11 | 0.0023400 | ** |
| 821.6139_9.026 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.331717 | 11 | 0.0011900 | ** |
| 822.5774_8.941 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.675168 | 11 | 0.0216000 | * |
| 823.6294_9.979 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.692842 | 11 | 0.0006580 | *** |
| 824.5433_5.925 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.818684 | 11 | 0.0167000 | * |
| 824.5771_8.938 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.480650 | 11 | 0.0051400 | ** |
| 824.5789_7.841 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.800002 | 11 | 0.0005530 | *** |
| 826.5606_6.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.320523 | 11 | 0.0405000 | * |
| 827.6272_7.905 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.811589 | 11 | 0.0028800 | ** |
| 828.5668_8.612 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.366137 | 11 | 0.0011200 | ** |
| 828.5759_7.19 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.219035 | 11 | 0.0081700 | ** |
| 828.5759_7.339 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.906686 | 11 | 0.0143000 | * |
| 828.6559_10.414 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.170122 | 11 | 0.0089200 | ** |
| 829.6431_8.816 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.927992 | 11 | 0.0137000 | * |
| 829.6432_9.024 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.504711 | 11 | 0.0008940 | *** |
| 830.5815_7.188 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.847985 | 11 | 0.0159000 | * |
| 831.6576_9.28 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.136092 | 11 | 0.0016500 | ** |
| 831.659_9.98 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.910747 | 11 | 0.0024300 | ** |
| 832.6067_8.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.813332 | 11 | 0.0169000 | * |
| 832.6428_10.551 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.972107 | 11 | 0.0127000 | * |
| 833.5544_7.557 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.563603 | 11 | 0.0044400 | ** |
| 833.6294_9.395 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.704948 | 11 | 0.0034700 | ** |
| 834.5643_7.242 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.484852 | 11 | 0.0303000 | * |
| 835.5322_8.336 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.707655 | 11 | 0.0006420 | *** |
| 835.6457_10.444 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.471447 | 11 | 0.0310000 | * |
| 836.5434_6.295 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.757764 | 11 | 0.0186000 | * |
| 837.6571_10.552 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.780186 | 11 | 0.0179000 | * |
| 838.5956_8.612 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.656729 | 11 | 0.0223000 | * |
| 840.5304_6.407 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.822264 | 11 | 0.0028300 | ** |
| 840.5749_7.321 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.340764 | 11 | 0.0065800 | ** |
| 840.5749_7.471 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.627852 | 11 | 0.0007310 | *** |
| 841.5661_6.75 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.719296 | 11 | 0.0200000 | * |
| 841.6406_8.452 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.113938 | 11 | 0.0017200 | ** |
| 842.5154_6.099 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.684742 | 11 | 0.0212000 | * |
| 842.5905_7.891 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.641280 | 11 | 0.0007150 | *** |
| 843.6586_9.396 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.736375 | 11 | 0.0006130 | *** |
| 845.5636_7.343 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.440275 | 11 | 0.0055200 | ** |
| 845.6746_10.439 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.292469 | 11 | 0.0426000 | * |
| 846.6588_10.643 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.546346 | 11 | 0.0045800 | ** |
| 847.6304_9.032 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.535210 | 11 | 0.0277000 | * |
| 847.6455_10.298 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.288894 | 11 | 0.0012800 | ** |
| 848.5114_9.046 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.722530 | 11 | 0.0033700 | ** |
| 848.577_8.458 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.528468 | 11 | 0.0280000 | * |
| 849.6462_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.677868 | 11 | 0.0036400 | ** |
| 849.6616_10.589 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.277192 | 11 | 0.0013000 | ** |
| 850.5595_6.407 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.652950 | 11 | 0.0007020 | *** |
| 851.6615_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.948328 | 11 | 0.0132000 | * |
| 853.619_10.659 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.213208 | 11 | 0.0014500 | ** |
| 853.6428_8.12 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.388563 | 11 | 0.0002200 | *** |
| 854.5519_7.576 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.247337 | 11 | 0.0013700 | ** |
| 854.5906_7.701 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.798355 | 11 | 0.0029500 | ** |
| 854.5918_8.021 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.961798 | 11 | 0.0129000 | * |
| 855.59_8.755 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.636432 | 11 | 0.0231000 | * |
| 855.6591_9.033 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.069368 | 11 | 0.0107000 | * |
| 856.6051_8.21 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.387966 | 11 | 0.0360000 | * |
| 856.6871_10.641 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.595975 | 11 | 0.0007700 | *** |
| 857.6737_9.513 | rel_abund_log2 | b1 | b3 | 12 | 12 | -9.480190 | 11 | 0.0000013 | **** |
| 857.6746_10.294 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.673240 | 11 | 0.0036700 | ** |
| 857.6749_9.986 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.130171 | 11 | 0.0095700 | ** |
| 859.5338_7.864 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.642053 | 11 | 0.0229000 | * |
| 859.6907_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.673498 | 11 | 0.0036700 | ** |
| 860.6369_10.19 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.291580 | 11 | 0.0012700 | ** |
| 860.6919_10.384 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.527332 | 11 | 0.0001790 | *** |
| 862.5261_7.47 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.872158 | 11 | 0.0026000 | ** |
| 862.527_7.471 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.919768 | 11 | 0.0023900 | ** |
| 862.6535_10.583 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.224550 | 11 | 0.0480000 | * |
| 862.664_10.445 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.523894 | 11 | 0.0283000 | * |
| 863.5644_9.582 | rel_abund_log2 | b1 | b3 | 12 | 12 | 9.018235 | 11 | 0.0000021 | **** |
| 863.6765_10.683 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.585805 | 11 | 0.0007830 | *** |
| 864.5747_7.263 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.456400 | 11 | 0.0319000 | * |
| 865.6758_10.678 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.778335 | 11 | 0.0180000 | * |
| 866.6268_8.879 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.714971 | 11 | 0.0201000 | * |
| 868.5303_7.476 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.594796 | 11 | 0.0249000 | * |
| 870.5618_7.814 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.787781 | 11 | 0.0177000 | * |
| 871.5805_8.017 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.059198 | 11 | 0.0109000 | * |
| 871.6147_8.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.383383 | 11 | 0.0363000 | * |
| 871.6891_10.337 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.286003 | 11 | 0.0000595 | **** |
| 872.5626_7.769 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.846966 | 11 | 0.0159000 | * |
| 872.6815_10.583 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.762710 | 11 | 0.0185000 | * |
| 874.559_6.259 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.845618 | 11 | 0.0159000 | * |
| 874.6643_9.99 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.701647 | 11 | 0.0206000 | * |
| 876.5748_6.674 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.416498 | 11 | 0.0342000 | * |
| 878.584_5.681 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.518898 | 11 | 0.0285000 | * |
| 878.5903_7.305 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.052135 | 11 | 0.0003710 | *** |
| 880.5375_7.198 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.564089 | 11 | 0.0044400 | ** |
| 880.6062_8.517 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.745637 | 11 | 0.0190000 | * |
| 881.5397_7.202 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.452148 | 11 | 0.0054100 | ** |
| 881.6055_9.023 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.594388 | 11 | 0.0042100 | ** |
| 882.6209_8.855 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.591754 | 11 | 0.0251000 | * |
| 883.5331_7.753 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.973553 | 11 | 0.0127000 | * |
| 883.6211_9.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.979468 | 11 | 0.0021600 | ** |
| 884.5587_7.554 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.240891 | 11 | 0.0466000 | * |
| 884.5687_8.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.732280 | 11 | 0.0033100 | ** |
| 886.5195_6.961 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.258508 | 11 | 0.0452000 | * |
| 886.5331_7.335 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.809623 | 11 | 0.0170000 | * |
| 887.5991_9.03 | rel_abund_log2 | b1 | b3 | 12 | 12 | 7.018997 | 11 | 0.0000221 | **** |
| 887.599_8.816 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.525994 | 11 | 0.0282000 | * |
| 889.617_9.974 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.263960 | 11 | 0.0075500 | ** |
| 890.5649_8.939 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.170605 | 11 | 0.0089100 | ** |
| 896.5624_7.191 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.864182 | 11 | 0.0154000 | * |
| 896.6737_10.623 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.612493 | 11 | 0.0040800 | ** |
| 897.6305_9.024 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.528349 | 11 | 0.0047300 | ** |
| 904.6171_10.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.787425 | 11 | 0.0177000 | * |
| 906.5531_8.016 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.208504 | 11 | 0.0493000 | * |
| 909.6366_10.295 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.494400 | 11 | 0.0050200 | ** |
| 909.6368_9.99 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.158108 | 11 | 0.0091100 | ** |
| 911.5645_8.686 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.330918 | 11 | 0.0398000 | * |
| 911.6461_9.397 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.856518 | 11 | 0.0156000 | * |
| 913.6162_9.035 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.934788 | 11 | 0.0136000 | * |
| 913.6613_10.438 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.251360 | 11 | 0.0458000 | * |
| 914.5643_8.457 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.333356 | 11 | 0.0396000 | * |
| 915.6319_9.987 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.421769 | 11 | 0.0339000 | * |
| 915.6327_10.293 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.703322 | 11 | 0.0205000 | * |
| 917.5422_7.049 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.860671 | 11 | 0.0155000 | * |
| 917.6329_9.989 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.975171 | 11 | 0.0021800 | ** |
| 917.633_10.292 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.658804 | 11 | 0.0222000 | * |
| 917.6482_10.589 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.600068 | 11 | 0.0247000 | * |
| 918.5466_6.408 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.353783 | 11 | 0.0382000 | * |
| 919.2277_0.616 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.835237 | 11 | 0.0162000 | * |
| 920.6735_10.31 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.161928 | 11 | 0.0090500 | ** |
| 922.5024_7.08 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.346660 | 11 | 0.0387000 | * |
| 922.5774_7.695 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.605611 | 11 | 0.0244000 | * |
| 923.646_9.035 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.811329 | 11 | 0.0169000 | * |
| 923.6524_10.353 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.250402 | 11 | 0.0013600 | ** |
| 924.5935_8.459 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.831952 | 11 | 0.0163000 | * |
| 925.6617_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.413678 | 11 | 0.0057900 | ** |
| 927.677_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.768684 | 11 | 0.0031100 | ** |
| 93.0344_0.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.767441 | 11 | 0.0183000 | * |
| 931.5514_9.582 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.307733 | 11 | 0.0000577 | **** |
| 931.6162_9.992 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.439948 | 11 | 0.0055300 | ** |
| 931.6162_9.999 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.523352 | 11 | 0.0047700 | ** |
| 933.5855_8.781 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.206123 | 11 | 0.0496000 | * |
| 934.5326_7.772 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.827829 | 11 | 0.0164000 | * |
| 939.6769_10.335 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.349194 | 11 | 0.0002340 | *** |
| 940.5496_7.764 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.603873 | 11 | 0.0245000 | * |
| 942.5548_7.339 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.096144 | 11 | 0.0102000 | * |
| 945.6385_9.975 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.023253 | 11 | 0.0003880 | *** |
| 948.5172_7.192 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.211315 | 11 | 0.0491000 | * |
| 950.6082_8.859 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.697007 | 11 | 0.0035200 | ** |
| 958.5324_7.191 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.769190 | 11 | 0.0005810 | *** |
| 959.6018_9.026 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.011016 | 11 | 0.0118000 | * |
| 960.7412_11.038 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.355291 | 11 | 0.0002320 | *** |
| 961.6178_9.977 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.863790 | 11 | 0.0154000 | * |
| 967.6322_9.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.507409 | 11 | 0.0049100 | ** |
| 968.581_8.937 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.760953 | 11 | 0.0185000 | * |
| 969.6384_9.033 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.966128 | 11 | 0.0128000 | * |
| 970.5858_8.459 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.377833 | 11 | 0.0366000 | * |
| 970.6153_10.616 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.245496 | 11 | 0.0078000 | ** |
| 971.6542_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.893852 | 11 | 0.0146000 | * |
| 976.5477_8.457 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.850174 | 11 | 0.0158000 | * |
| 983.6184_9.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.674388 | 11 | 0.0036600 | ** |
| 983.6185_9.993 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.741676 | 11 | 0.0192000 | * |
| 985.616_9.034 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.202084 | 11 | 0.0499000 | * |
| 986.5637_8.459 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.993769 | 11 | 0.0004070 | *** |
| 987.6318_9.987 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.215418 | 11 | 0.0082300 | ** |
| 990.5651_8.013 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.503305 | 11 | 0.0293000 | * |
| 992.5806_8.459 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.293004 | 11 | 0.0012700 | ** |
| 993.6488_9.986 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.603555 | 11 | 0.0245000 | * |
## [1] 311
# let's grab both datasets for features sig in pre v post beta and pre v post control and combine them
fulljoin_t.test_betaANDctrl <- full_join(beta_t.test_paired_sig, ctrl_t.test_paired_sig,
by = "mz_rt",
suffix = c(".beta", ".ctrl"))
# number of features in combined list
nrow(fulljoin_t.test_betaANDctrl)## [1] 389
Let’s try and remove features significant due to the background diet
sig_paired_beta_rmBG <- fulljoin_t.test_betaANDctrl %>%
# add a column to account for direction
mutate(sign = statistic.beta * statistic.ctrl) %>%
# replace NAs in the sign column with 0
mutate(sign = replace_na(sign, 0)) %>%
# replace NAs in the statistic.ctrl column to 0
mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
# filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
filter((sign < 0 | statistic.ctrl == 0))
# number of features without bg diet effect
nrow(sig_paired_beta_rmBG)## [1] 167
How many BG-diet-related features did we remove from the list of significant beta features?
## [1] 144
Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)
# select only features from paired list that have a match in ANOVA list
beta_sig_ANOVA_overlap_paired <- inner_join(sig_paired_beta_rmBG,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# features overlapping with sig ANOVA
unique(beta_sig_ANOVA_overlap_paired$mz_rt)## [1] "1561.2028_9.038" "526.3144_2.683" "674.6039_10.834" "682.6332_10.835"
## [5] "705.5178_5.308" "721.549_6" "764.5798_8.343" "800.5357_7.469"
## [9] "824.5771_8.938" "847.6455_10.298" "848.5114_9.046" "853.619_10.659"
## [13] "857.6749_9.986" "863.5644_9.582" "868.5303_7.476" "887.5991_9.03"
## [17] "909.6368_9.99" "915.6327_10.293" "917.6329_9.989" "940.5496_7.764"
## [21] "945.6385_9.975" "969.6384_9.033" "983.6184_9.976" "983.6185_9.993"
## [25] "987.6318_9.987"
Pulling features that are significant in paired t-test due to treatment, not due to background diet.
paired_beta_for_mummichog <- sig_paired_beta_rmBG %>%
dplyr::select(mz_rt,
p.beta,
statistic.beta) %>%
separate(col = mz_rt,
into = c("m/z", "rt"),
sep = "_") %>%
rename("p-value" = "p.beta") %>%
rename("t-score" = "statistic.beta")
write_csv(paired_beta_for_mummichog,
"for mummichog analysis/t-test-res-beta-paired.csv")# run paired t-tests for control intervention
red_t.test_paired <- df_for_stats %>%
filter(treatment == "red") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
red_t.test_paired_sig <- red_t.test_paired %>%
filter(p < 0.05)
kable(red_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1002.5196_7.769 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.087474 | 11 | 0.0003510 | *** |
| 1008.5435_7.813 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.484718 | 11 | 0.0303000 | * |
| 1061.6776_3.089 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.255507 | 11 | 0.0454000 | * |
| 1135.6242_2.73 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.839018 | 11 | 0.0161000 | * |
| 117.0557_0.669 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.518094 | 11 | 0.0048100 | ** |
| 1279.8241_10.52 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.411636 | 11 | 0.0345000 | * |
| 1334.2417_10.855 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.677224 | 11 | 0.0215000 | * |
| 1335.245_10.854 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.512060 | 11 | 0.0289000 | * |
| 1554.1226_7.773 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.891711 | 11 | 0.0025100 | ** |
| 1555.1258_7.773 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.649203 | 11 | 0.0226000 | * |
| 1561.2028_9.038 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.406847 | 11 | 0.0010500 | ** |
| 1579.1408_6.75 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.328366 | 11 | 0.0400000 | * |
| 1600.0951_6.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.280807 | 11 | 0.0435000 | * |
| 1616.1136_7.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.714983 | 11 | 0.0034100 | ** |
| 1632.139_7.776 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.115819 | 11 | 0.0098200 | ** |
| 1633.1422_7.775 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.862239 | 11 | 0.0155000 | * |
| 1654.1535_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.315537 | 11 | 0.0068900 | ** |
| 1655.1569_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.281514 | 11 | 0.0434000 | * |
| 1666.3553_10.589 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.272183 | 11 | 0.0441000 | * |
| 201.0226_0.688 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.106809 | 11 | 0.0099800 | ** |
| 213.0223_0.689 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.881212 | 11 | 0.0001060 | *** |
| 231.0795_0.662 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.314161 | 11 | 0.0410000 | * |
| 239.0923_0.975 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.170083 | 11 | 0.0015600 | ** |
| 279.2327_4.208 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.327239 | 11 | 0.0401000 | * |
| 311.2224_2.465 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.592636 | 11 | 0.0042200 | ** |
| 316.9477_0.518 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.644557 | 11 | 0.0228000 | * |
| 343.9948_0.639 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.386287 | 11 | 0.0361000 | * |
| 352.0856_0.687 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.710290 | 11 | 0.0034400 | ** |
| 397.3681_5 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.343181 | 11 | 0.0390000 | * |
| 411.3837_5.349 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.978168 | 11 | 0.0004170 | *** |
| 413.1998_0.688 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.742834 | 11 | 0.0191000 | * |
| 437.0542_0.688 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.526373 | 11 | 0.0282000 | * |
| 441.3942_4.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.948416 | 11 | 0.0022800 | ** |
| 444.0682_4.208 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.387538 | 11 | 0.0360000 | * |
| 447.3471_4.631 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.917431 | 11 | 0.0140000 | * |
| 462.1763_0.655 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.921660 | 11 | 0.0023900 | ** |
| 478.2933_3.354 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.510513 | 11 | 0.0048800 | ** |
| 491.3733_4.784 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.573233 | 11 | 0.0259000 | * |
| 493.3349_5.699 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.437924 | 11 | 0.0329000 | * |
| 504.3089_2.726 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.726291 | 11 | 0.0197000 | * |
| 506.3245_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.764072 | 11 | 0.0184000 | * |
| 507.223_1.7 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.642237 | 11 | 0.0000365 | **** |
| 507.223_2.273 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.376197 | 11 | 0.0002250 | *** |
| 538.3144_2.488 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.752197 | 11 | 0.0188000 | * |
| 539.4309_4.416 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.738945 | 11 | 0.0193000 | * |
| 551.3582_4.841 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.655791 | 11 | 0.0001480 | *** |
| 557.4566_6.309 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.960029 | 11 | 0.0004290 | *** |
| 557.457_6.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.928513 | 11 | 0.0000989 | **** |
| 558.3148_3.234 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.722424 | 11 | 0.0198000 | * |
| 559.4719_6.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.504786 | 11 | 0.0008940 | *** |
| 561.2272_6.889 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.524246 | 11 | 0.0008660 | *** |
| 566.3462_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.698232 | 11 | 0.0035100 | ** |
| 567.4621_4.643 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.258499 | 11 | 0.0013500 | ** |
| 573.4515_4.266 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.883000 | 11 | 0.0025500 | ** |
| 573.4515_4.873 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.950609 | 11 | 0.0004350 | *** |
| 575.4672_5.354 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.966417 | 11 | 0.0000937 | **** |
| 575.467_4.662 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.324484 | 11 | 0.0012100 | ** |
| 577.3738_4.918 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.802641 | 11 | 0.0005510 | *** |
| 579.3893_5.557 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.522476 | 11 | 0.0008690 | *** |
| 586.3143_2.221 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.296927 | 11 | 0.0423000 | * |
| 591.3894_5.174 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.279564 | 11 | 0.0436000 | * |
| 591.462_4.314 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.996007 | 11 | 0.0004050 | *** |
| 591.462_4.651 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.759642 | 11 | 0.0005900 | *** |
| 593.4778_4.664 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.664055 | 11 | 0.0000354 | **** |
| 594.3768_3.858 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.493352 | 11 | 0.0299000 | * |
| 595.4933_4.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | 8.808419 | 11 | 0.0000026 | **** |
| 605.1555_0.616 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.914407 | 11 | 0.0141000 | * |
| 618.308_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.368440 | 11 | 0.0373000 | * |
| 624.3379_0.689 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.822007 | 11 | 0.0028300 | ** |
| 624.3382_0.91 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.731510 | 11 | 0.0195000 | * |
| 630.5417_10.49 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.427459 | 11 | 0.0336000 | * |
| 634.3329_3.232 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.244903 | 11 | 0.0013800 | ** |
| 652.5879_10.611 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.216051 | 11 | 0.0487000 | * |
| 653.491_10.071 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.500840 | 11 | 0.0295000 | * |
| 658.0669_0.628 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.586947 | 11 | 0.0253000 | * |
| 676.6243_10.731 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.997688 | 11 | 0.0004040 | *** |
| 680.5744_10.545 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.790812 | 11 | 0.0176000 | * |
| 682.5753_10.545 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.394082 | 11 | 0.0059900 | ** |
| 684.6065_10.853 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.741460 | 11 | 0.0192000 | * |
| 690.603_10.546 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.866297 | 11 | 0.0153000 | * |
| 696.3033_3.233 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.120607 | 11 | 0.0097400 | ** |
| 696.6064_10.749 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.216071 | 11 | 0.0014500 | ** |
| 720.4962_7.178 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.222434 | 11 | 0.0482000 | * |
| 736.5275_8.432 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.108040 | 11 | 0.0099600 | ** |
| 746.5966_10.853 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.532297 | 11 | 0.0279000 | * |
| 750.5436_9.042 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.428826 | 11 | 0.0335000 | * |
| 759.5646_6.287 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.359414 | 11 | 0.0063700 | ** |
| 763.5516_6.749 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.605778 | 11 | 0.0244000 | * |
| 764.5798_8.343 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.697964 | 11 | 0.0207000 | * |
| 766.5385_8.385 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.278484 | 11 | 0.0437000 | * |
| 773.5806_6.749 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.202953 | 11 | 0.0014800 | ** |
| 776.5438_6.762 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.234046 | 11 | 0.0472000 | * |
| 780.4705_7.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.371311 | 11 | 0.0371000 | * |
| 786.5646_7.575 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.674363 | 11 | 0.0216000 | * |
| 789.6114_7.911 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.894935 | 11 | 0.0146000 | * |
| 790.5382_8.16 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.710232 | 11 | 0.0203000 | * |
| 790.5593_7.252 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.392407 | 11 | 0.0357000 | * |
| 793.5985_8.765 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.083592 | 11 | 0.0018100 | ** |
| 794.5298_7.047 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.470229 | 11 | 0.0311000 | * |
| 794.5464_7.772 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.425982 | 11 | 0.0010200 | ** |
| 795.5979_8.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.910540 | 11 | 0.0142000 | * |
| 796.5458_7.771 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.334957 | 11 | 0.0066500 | ** |
| 800.5357_7.469 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.935130 | 11 | 0.0136000 | * |
| 803.6275_8.771 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.380422 | 11 | 0.0365000 | * |
| 804.5762_7.774 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.761839 | 11 | 0.0005880 | *** |
| 804.6308_8.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.708314 | 11 | 0.0204000 | * |
| 806.5463_7.553 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.657083 | 11 | 0.0223000 | * |
| 810.5642_6.968 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.864907 | 11 | 0.0154000 | * |
| 810.5647_7.467 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.036476 | 11 | 0.0000847 | **** |
| 812.5436_6.468 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.288893 | 11 | 0.0429000 | * |
| 812.58_7.898 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.972847 | 11 | 0.0127000 | * |
| 815.627_8.417 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.539421 | 11 | 0.0275000 | * |
| 818.6271_10.414 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.627604 | 11 | 0.0235000 | * |
| 819.6141_8.816 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.470010 | 11 | 0.0311000 | * |
| 819.6141_9.024 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.336529 | 11 | 0.0394000 | * |
| 820.4801_7.866 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.958164 | 11 | 0.0130000 | * |
| 821.6139_9.026 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.130582 | 11 | 0.0095700 | ** |
| 822.5774_8.941 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.223471 | 11 | 0.0081100 | ** |
| 824.5771_8.938 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.959486 | 11 | 0.0130000 | * |
| 824.5789_7.841 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.511575 | 11 | 0.0001830 | *** |
| 827.6272_7.905 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.458125 | 11 | 0.0000469 | **** |
| 828.6559_10.414 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.674948 | 11 | 0.0216000 | * |
| 829.6431_8.816 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.604246 | 11 | 0.0245000 | * |
| 829.6432_9.024 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.546679 | 11 | 0.0272000 | * |
| 830.5815_7.188 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.286961 | 11 | 0.0430000 | * |
| 831.6576_9.28 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.683310 | 11 | 0.0036000 | ** |
| 831.659_9.98 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.594857 | 11 | 0.0249000 | * |
| 832.6067_8.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.286437 | 11 | 0.0430000 | * |
| 833.6294_9.395 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.469274 | 11 | 0.0052500 | ** |
| 834.5643_7.242 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.396780 | 11 | 0.0059600 | ** |
| 835.5322_8.336 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.912013 | 11 | 0.0024300 | ** |
| 836.5799_7.605 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.290278 | 11 | 0.0072000 | ** |
| 838.5955_8.279 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.243569 | 11 | 0.0078200 | ** |
| 838.5956_7.75 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.958585 | 11 | 0.0130000 | * |
| 838.5956_8.612 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.589499 | 11 | 0.0252000 | * |
| 840.5304_6.407 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.607587 | 11 | 0.0244000 | * |
| 840.5749_7.321 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.328660 | 11 | 0.0400000 | * |
| 840.5749_7.471 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.952872 | 11 | 0.0022600 | ** |
| 840.6112_8.445 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.665196 | 11 | 0.0220000 | * |
| 841.5661_6.75 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.798294 | 11 | 0.0173000 | * |
| 841.6406_8.452 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.142839 | 11 | 0.0016400 | ** |
| 842.5905_7.891 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.893307 | 11 | 0.0146000 | * |
| 843.5493_6.926 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.532832 | 11 | 0.0278000 | * |
| 843.6586_9.396 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.632265 | 11 | 0.0007260 | *** |
| 844.562_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.899062 | 11 | 0.0145000 | * |
| 846.5617_8.018 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.362157 | 11 | 0.0377000 | * |
| 851.6615_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.365918 | 11 | 0.0374000 | * |
| 853.619_10.659 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.959340 | 11 | 0.0130000 | * |
| 853.6428_8.12 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.439360 | 11 | 0.0002040 | *** |
| 854.5519_7.576 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.759487 | 11 | 0.0186000 | * |
| 854.5918_8.021 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.640217 | 11 | 0.0230000 | * |
| 854.6714_10.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.934783 | 11 | 0.0136000 | * |
| 855.59_8.755 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.782645 | 11 | 0.0178000 | * |
| 855.6591_9.033 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.370710 | 11 | 0.0062400 | ** |
| 856.5377_7.769 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.819373 | 11 | 0.0167000 | * |
| 857.6737_9.513 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.017278 | 11 | 0.0020300 | ** |
| 857.6749_9.986 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.519018 | 11 | 0.0285000 | * |
| 859.6907_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.507800 | 11 | 0.0291000 | * |
| 860.6919_10.384 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.220171 | 11 | 0.0484000 | * |
| 862.5261_7.47 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.442545 | 11 | 0.0327000 | * |
| 862.527_7.471 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.581277 | 11 | 0.0043100 | ** |
| 862.5335_7.761 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.484913 | 11 | 0.0051000 | ** |
| 862.6535_10.583 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.461363 | 11 | 0.0316000 | * |
| 863.5644_9.582 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.513263 | 11 | 0.0008820 | *** |
| 864.5747_7.263 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.194692 | 11 | 0.0015000 | ** |
| 867.5493_6.755 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.270510 | 11 | 0.0074600 | ** |
| 868.5303_7.476 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.161248 | 11 | 0.0003130 | *** |
| 870.5618_7.814 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.517540 | 11 | 0.0286000 | * |
| 871.6147_8.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.998878 | 11 | 0.0121000 | * |
| 871.6891_10.337 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.058263 | 11 | 0.0000821 | **** |
| 872.5626_7.769 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.662368 | 11 | 0.0006910 | *** |
| 872.6815_10.583 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.303731 | 11 | 0.0418000 | * |
| 878.5903_7.305 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.993849 | 11 | 0.0122000 | * |
| 878.5909_7.805 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.196243 | 11 | 0.0085100 | ** |
| 880.5669_7.637 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.649512 | 11 | 0.0226000 | * |
| 880.6062_8.517 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.497099 | 11 | 0.0297000 | * |
| 881.6055_9.023 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.446621 | 11 | 0.0324000 | * |
| 882.6209_8.855 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.384586 | 11 | 0.0362000 | * |
| 883.5565_8.118 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.119995 | 11 | 0.0097500 | ** |
| 883.6211_9.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.980937 | 11 | 0.0125000 | * |
| 884.5687_8.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.947014 | 11 | 0.0133000 | * |
| 887.5646_9.463 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.692579 | 11 | 0.0209000 | * |
| 887.5991_9.03 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.089693 | 11 | 0.0103000 | * |
| 888.5362_7.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.724193 | 11 | 0.0033600 | ** |
| 894.658_10.086 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.874958 | 11 | 0.0151000 | * |
| 905.6039_10.298 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.489882 | 11 | 0.0300000 | * |
| 906.5828_8.614 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.212480 | 11 | 0.0490000 | * |
| 909.6368_9.99 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.678270 | 11 | 0.0215000 | * |
| 911.6461_9.397 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.091993 | 11 | 0.0102000 | * |
| 914.6592_10.621 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.615078 | 11 | 0.0240000 | * |
| 915.6327_10.293 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.804097 | 11 | 0.0171000 | * |
| 918.5543_7.768 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.563219 | 11 | 0.0008120 | *** |
| 918.5546_7.77 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.609386 | 11 | 0.0001580 | *** |
| 919.2277_0.616 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.779683 | 11 | 0.0179000 | * |
| 919.6483_10.59 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.490412 | 11 | 0.0300000 | * |
| 920.6735_10.31 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.883455 | 11 | 0.0001060 | *** |
| 922.5781_8.016 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.273357 | 11 | 0.0074200 | ** |
| 922.6891_10.613 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.865719 | 11 | 0.0154000 | * |
| 923.646_9.035 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.004914 | 11 | 0.0120000 | * |
| 923.6524_10.353 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.830168 | 11 | 0.0005270 | *** |
| 925.6617_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.595588 | 11 | 0.0249000 | * |
| 930.5504_7.815 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.151413 | 11 | 0.0092200 | ** |
| 931.5514_9.582 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.949634 | 11 | 0.0022700 | ** |
| 938.5513_8.018 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.411316 | 11 | 0.0345000 | * |
| 939.6769_10.335 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.723151 | 11 | 0.0033600 | ** |
| 940.5496_7.764 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.698203 | 11 | 0.0207000 | * |
| 945.6385_9.975 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.334111 | 11 | 0.0396000 | * |
| 959.6018_9.026 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.230706 | 11 | 0.0475000 | * |
| 967.6322_9.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.648209 | 11 | 0.0227000 | * |
| 968.581_8.937 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.602143 | 11 | 0.0246000 | * |
| 971.6542_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.216284 | 11 | 0.0082100 | ** |
| 983.6184_9.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.773685 | 11 | 0.0181000 | * |
| 983.6185_9.993 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.688211 | 11 | 0.0211000 | * |
| 984.5483_8.018 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.450959 | 11 | 0.0322000 | * |
| 985.616_9.034 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.314878 | 11 | 0.0409000 | * |
| 993.6487_10.29 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.001124 | 11 | 0.0121000 | * |
| 993.6488_9.986 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.060578 | 11 | 0.0108000 | * |
## [1] 217
# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_redANDctrl <- full_join(red_t.test_paired_sig, ctrl_t.test_paired_sig,
by = "mz_rt",
suffix = c(".red", ".ctrl"))
# number of features in combined list
nrow(fulljoin_t.test_redANDctrl)## [1] 317
Let’s try and remove features significant due to the background diet
sig_paired_red_rmBG <- fulljoin_t.test_redANDctrl %>%
# add a column to account for direction
mutate(sign = statistic.red * statistic.ctrl) %>%
# replace NAs in the sign column with 0
mutate(sign = replace_na(sign, 0)) %>%
# replace NAs in the statistic.ctrl column to 0
mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
# filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
filter((sign < 0 | statistic.ctrl == 0))
# number of features without bg diet effect
nrow(sig_paired_red_rmBG)## [1] 95
How many BG-diet-related features did we remove from the list of significant beta features?
## [1] 122
Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)
# select only features from paired list that have a match in ANOVA list
red_sig_ANOVA_overlap_paired <- inner_join(sig_paired_red_rmBG,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# features overlapping with sig ANOVA
unique(red_sig_ANOVA_overlap_paired$mz_rt)## [1] "1002.5196_7.769" "1561.2028_9.038" "561.2272_6.889" "736.5275_8.432"
## [5] "764.5798_8.343" "800.5357_7.469" "824.5771_8.938" "853.619_10.659"
## [9] "857.6749_9.986" "862.5335_7.761" "863.5644_9.582" "868.5303_7.476"
## [13] "887.5991_9.03" "909.6368_9.99" "915.6327_10.293" "940.5496_7.764"
## [17] "945.6385_9.975" "983.6184_9.976" "983.6185_9.993"
Pulling features that are significant in paired t-test due to treatment, not due to background diet.
paired_red_for_mummichog <- sig_paired_red_rmBG %>%
dplyr::select(mz_rt,
p.red,
statistic.red) %>%
separate(col = mz_rt,
into = c("m/z", "rt"),
sep = "_") %>%
rename("p-value" = "p.red") %>%
rename("t-score" = "statistic.red")
write_csv(paired_red_for_mummichog,
"for mummichog analysis/t-test-res-red-paired.csv")# run paired t-tests for control intervention
tomato_t.test_paired <- df_for_stats %>%
filter(tomato_or_control == "tomato") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
tomato_t.test_paired_sig <- tomato_t.test_paired %>%
filter(p < 0.05)
kable(tomato_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1002.5196_7.769 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.363569 | 23 | 0.0002270 | *** |
| 1008.5435_7.813 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.718844 | 23 | 0.0122000 | * |
| 1016.7241_9.858 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.324759 | 23 | 0.0293000 | * |
| 103.04_0.651 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.612068 | 23 | 0.0156000 | * |
| 1061.6776_3.089 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.500347 | 23 | 0.0200000 | * |
| 1135.6242_2.73 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.252609 | 23 | 0.0341000 | * |
| 117.0557_0.669 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.301816 | 23 | 0.0002650 | *** |
| 1235.798_10.088 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.621783 | 23 | 0.0014300 | ** |
| 1261.8136_10.084 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.623333 | 23 | 0.0152000 | * |
| 1263.8295_10.575 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.387722 | 23 | 0.0256000 | * |
| 1279.8241_10.52 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.745093 | 23 | 0.0010600 | ** |
| 1334.2417_10.855 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.785577 | 23 | 0.0009570 | *** |
| 1335.245_10.854 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.241285 | 23 | 0.0003090 | *** |
| 1554.1226_7.773 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.382053 | 23 | 0.0002170 | *** |
| 1555.1258_7.773 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.206343 | 23 | 0.0039200 | ** |
| 1558.1192_8.016 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.354922 | 23 | 0.0274000 | * |
| 1561.2028_9.038 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.762764 | 23 | 0.0000072 | **** |
| 1579.1408_6.75 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.006221 | 23 | 0.0063000 | ** |
| 1584.1204_6.981 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.145271 | 23 | 0.0427000 | * |
| 1585.1238_6.981 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.143270 | 23 | 0.0429000 | * |
| 1598.0913_6.94 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.422202 | 23 | 0.0237000 | * |
| 1598.1714_7.758 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.904506 | 23 | 0.0079900 | ** |
| 1599.0952_6.94 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.215507 | 23 | 0.0369000 | * |
| 1600.0951_6.94 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.689580 | 23 | 0.0131000 | * |
| 1602.1228_7.339 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.403199 | 23 | 0.0247000 | * |
| 1612.1504_7.187 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.617357 | 23 | 0.0154000 | * |
| 1616.1136_7.768 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.301321 | 23 | 0.0002660 | *** |
| 1632.139_7.776 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.622438 | 23 | 0.0014300 | ** |
| 1633.1422_7.775 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.522504 | 23 | 0.0001530 | *** |
| 1654.1535_8.02 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.929780 | 23 | 0.0075300 | ** |
| 1655.1569_8.02 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.281654 | 23 | 0.0032700 | ** |
| 1664.3521_10.59 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.040890 | 23 | 0.0005080 | *** |
| 1665.3553_10.589 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.286385 | 23 | 0.0032300 | ** |
| 1666.3553_10.589 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.182997 | 23 | 0.0041400 | ** |
| 1669.2155_8.46 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.172830 | 23 | 0.0403000 | * |
| 1670.349_9.987 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.026759 | 23 | 0.0060000 | ** |
| 1674.3803_10.59 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.712049 | 23 | 0.0011500 | ** |
| 1675.384_10.589 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.620181 | 23 | 0.0014400 | ** |
| 1685.1732_8.11 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.101121 | 23 | 0.0468000 | * |
| 172.9913_0.642 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.198641 | 23 | 0.0039900 | ** |
| 188.9862_0.644 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.065903 | 23 | 0.0054700 | ** |
| 201.0226_0.688 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.998360 | 23 | 0.0005650 | *** |
| 201.0575_0.644 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.171078 | 23 | 0.0405000 | * |
| 213.0223_0.689 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.881431 | 23 | 0.0084300 | ** |
| 226.9658_0.499 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.307105 | 23 | 0.0304000 | * |
| 229.0537_0.703 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.223511 | 23 | 0.0363000 | * |
| 230.9967_0.644 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.398109 | 23 | 0.0250000 | * |
| 239.0923_0.975 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.249157 | 23 | 0.0344000 | * |
| 244.908_0.706 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.051810 | 23 | 0.0056600 | ** |
| 311.2224_2.465 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.457428 | 23 | 0.0021400 | ** |
| 316.9477_0.518 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.481219 | 23 | 0.0208000 | * |
| 343.9948_0.639 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.534062 | 23 | 0.0185000 | * |
| 352.0856_0.687 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.668731 | 23 | 0.0000090 | **** |
| 397.3681_5 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.364590 | 23 | 0.0002270 | *** |
| 411.3837_5.349 | rel_abund_log2 | b1 | b3 | 24 | 24 | 8.048607 | 23 | 0.0000000 | **** |
| 413.1998_0.688 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.312217 | 23 | 0.0301000 | * |
| 436.2827_3.522 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.158151 | 23 | 0.0044000 | ** |
| 437.0542_0.688 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.140244 | 23 | 0.0003970 | *** |
| 441.3942_4.718 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.755661 | 23 | 0.0000073 | **** |
| 447.3471_4.631 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.640290 | 23 | 0.0146000 | * |
| 449.3627_4.663 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.657712 | 23 | 0.0141000 | * |
| 462.1763_0.655 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.698636 | 23 | 0.0000008 | **** |
| 467.3735_4.306 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.465112 | 23 | 0.0216000 | * |
| 476.2777_2.847 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.596805 | 23 | 0.0161000 | * |
| 478.2933_3.354 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.056524 | 23 | 0.0000405 | **** |
| 504.3089_2.726 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.204509 | 23 | 0.0039400 | ** |
| 506.3245_3.232 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.574233 | 23 | 0.0016100 | ** |
| 507.223_1.7 | rel_abund_log2 | b1 | b3 | 24 | 24 | -11.228326 | 23 | 0.0000000 | **** |
| 507.223_2.273 | rel_abund_log2 | b1 | b3 | 24 | 24 | -9.093738 | 23 | 0.0000000 | **** |
| 511.3997_4.453 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.746641 | 23 | 0.0115000 | * |
| 517.2436_2.68 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.524262 | 23 | 0.0190000 | * |
| 519.347_6.861 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.216985 | 23 | 0.0368000 | * |
| 524.3351_3.376 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.734986 | 23 | 0.0118000 | * |
| 526.3144_2.683 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.997438 | 23 | 0.0064300 | ** |
| 528.2631_0.691 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.972412 | 23 | 0.0068200 | ** |
| 537.4153_4.24 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.795157 | 23 | 0.0103000 | * |
| 539.4309_4.416 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.598005 | 23 | 0.0015200 | ** |
| 551.3582_4.841 | rel_abund_log2 | b1 | b3 | 24 | 24 | -9.941869 | 23 | 0.0000000 | **** |
| 554.3456_3.441 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.608189 | 23 | 0.0014800 | ** |
| 556.299_2.728 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.192282 | 23 | 0.0387000 | * |
| 557.4566_6.309 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.515541 | 23 | 0.0001560 | *** |
| 557.457_6.865 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.545420 | 23 | 0.0017300 | ** |
| 558.3148_3.234 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.885466 | 23 | 0.0007470 | *** |
| 559.4719_6.867 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.625960 | 23 | 0.0000009 | **** |
| 560.2269_6.891 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.847245 | 23 | 0.0091200 | ** |
| 564.3308_2.727 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.162660 | 23 | 0.0412000 | * |
| 566.3462_3.232 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.767304 | 23 | 0.0000831 | **** |
| 567.4621_4.643 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.042322 | 23 | 0.0000037 | **** |
| 573.4515_4.266 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.048715 | 23 | 0.0000036 | **** |
| 573.4515_4.873 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.490611 | 23 | 0.0001660 | *** |
| 575.4672_5.354 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.374904 | 23 | 0.0000002 | **** |
| 575.467_4.662 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.917587 | 23 | 0.0000005 | **** |
| 577.3738_4.918 | rel_abund_log2 | b1 | b3 | 24 | 24 | -9.700780 | 23 | 0.0000000 | **** |
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| 591.3894_5.174 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.036246 | 23 | 0.0000426 | **** |
| 591.462_4.314 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.970156 | 23 | 0.0000502 | **** |
| 591.462_4.651 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.404172 | 23 | 0.0000016 | **** |
| 593.4778_4.664 | rel_abund_log2 | b1 | b3 | 24 | 24 | 8.493080 | 23 | 0.0000000 | **** |
| 594.3768_3.858 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.620389 | 23 | 0.0014400 | ** |
| 595.4933_4.953 | rel_abund_log2 | b1 | b3 | 24 | 24 | 14.557359 | 23 | 0.0000000 | **** |
| 600.5119_9.48 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.683104 | 23 | 0.0133000 | * |
| 605.1555_0.616 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.092346 | 23 | 0.0004470 | *** |
| 618.308_3.232 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.341788 | 23 | 0.0028300 | ** |
| 619.2886_2.757 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.243276 | 23 | 0.0348000 | * |
| 624.3379_0.689 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.761801 | 23 | 0.0000843 | **** |
| 624.3382_0.91 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.332559 | 23 | 0.0288000 | * |
| 630.5417_10.49 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.228670 | 23 | 0.0359000 | * |
| 632.3174_2.729 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.142473 | 23 | 0.0430000 | * |
| 634.3329_3.232 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.891200 | 23 | 0.0000611 | **** |
| 640.5796_10.729 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.143170 | 23 | 0.0045600 | ** |
| 643.1067_0.611 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.161843 | 23 | 0.0413000 | * |
| 653.491_10.071 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.405533 | 23 | 0.0246000 | * |
| 654.5589_10.54 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.211313 | 23 | 0.0372000 | * |
| 656.5586_10.54 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.415822 | 23 | 0.0240000 | * |
| 656.575_10.694 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.219790 | 23 | 0.0037900 | ** |
| 658.0669_0.628 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.725567 | 23 | 0.0121000 | * |
| 664.5877_10.541 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.142568 | 23 | 0.0045600 | ** |
| 674.6039_10.834 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.290434 | 23 | 0.0002730 | *** |
| 676.6243_10.731 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.593231 | 23 | 0.0000010 | **** |
| 678.6402_10.885 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.188411 | 23 | 0.0040900 | ** |
| 680.0486_0.63 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.070573 | 23 | 0.0498000 | * |
| 680.5744_10.545 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.160390 | 23 | 0.0043700 | ** |
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| 682.6332_10.835 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.875103 | 23 | 0.0007670 | *** |
| 684.6065_10.853 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.358717 | 23 | 0.0002300 | *** |
| 690.603_10.546 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.129321 | 23 | 0.0047100 | ** |
| 696.3033_3.233 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.038067 | 23 | 0.0005120 | *** |
| 696.6064_10.749 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.829105 | 23 | 0.0000061 | **** |
| 698.3189_3.775 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.244151 | 23 | 0.0347000 | * |
| 698.5121_7.962 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.546788 | 23 | 0.0180000 | * |
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| 721.549_6 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.236895 | 23 | 0.0003120 | *** |
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| 746.5966_10.853 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.830749 | 23 | 0.0000710 | **** |
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| 750.5436_9.042 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.050904 | 23 | 0.0004960 | *** |
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| 759.5646_6.287 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.619967 | 23 | 0.0001200 | *** |
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| 762.5643_8.206 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.889935 | 23 | 0.0082600 | ** |
| 762.6216_10.854 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.036895 | 23 | 0.0058600 | ** |
| 763.5516_6.749 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.775025 | 23 | 0.0108000 | * |
| 764.5225_7.43 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.222037 | 23 | 0.0364000 | * |
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| 765.5735_6.098 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.360399 | 23 | 0.0271000 | * |
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| 773.5806_6.749 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.568133 | 23 | 0.0016300 | ** |
| 775.5965_7.563 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.125432 | 23 | 0.0445000 | * |
| 776.5351_7.576 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.164678 | 23 | 0.0043300 | ** |
| 776.5354_7.574 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.315384 | 23 | 0.0030200 | ** |
| 777.6118_7.986 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.243935 | 23 | 0.0035800 | ** |
| 780.4705_7.867 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.315718 | 23 | 0.0002560 | *** |
| 784.549_6.62 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.270647 | 23 | 0.0328000 | * |
| 786.5646_7.575 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.533708 | 23 | 0.0001490 | *** |
| 788.5795_7.719 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.124118 | 23 | 0.0446000 | * |
| 789.6114_7.911 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.741910 | 23 | 0.0000886 | **** |
| 789.6116_8.177 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.725136 | 23 | 0.0011100 | ** |
| 790.4993_7.863 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.861659 | 23 | 0.0007930 | *** |
| 790.5953_8.511 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.705806 | 23 | 0.0011600 | ** |
| 792.5765_7.938 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.111977 | 23 | 0.0458000 | * |
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| 793.5985_8.748 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.378696 | 23 | 0.0261000 | * |
| 793.5985_8.765 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.680943 | 23 | 0.0001030 | *** |
| 794.5464_7.772 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.067306 | 23 | 0.0004760 | *** |
| 795.5979_8.768 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.572121 | 23 | 0.0170000 | * |
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| 798.5279_6.032 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.165916 | 23 | 0.0409000 | * |
| 800.5357_7.469 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.605652 | 23 | 0.0001240 | *** |
| 803.6275_8.749 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.598700 | 23 | 0.0161000 | * |
| 803.6275_8.771 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.584389 | 23 | 0.0015700 | ** |
| 804.5762_7.774 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.059518 | 23 | 0.0000402 | **** |
| 804.6308_8.768 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.044895 | 23 | 0.0005030 | *** |
| 806.5463_7.553 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.935885 | 23 | 0.0074200 | ** |
| 808.5018_9.051 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.201062 | 23 | 0.0039700 | ** |
| 810.5642_6.968 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.438649 | 23 | 0.0229000 | * |
| 810.5647_7.467 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.482269 | 23 | 0.0000001 | **** |
| 812.5436_6.468 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.271119 | 23 | 0.0002860 | *** |
| 812.58_7.898 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.973914 | 23 | 0.0000498 | **** |
| 814.5957_8.732 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.604246 | 23 | 0.0159000 | * |
| 815.627_8.417 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.483325 | 23 | 0.0020100 | ** |
| 816.5751_7.553 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.742876 | 23 | 0.0116000 | * |
| 818.5302_6.479 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.365591 | 23 | 0.0268000 | * |
| 818.5304_6.915 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.557207 | 23 | 0.0176000 | * |
| 818.5462_7.192 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.835585 | 23 | 0.0093700 | ** |
| 818.5462_7.337 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.154764 | 23 | 0.0419000 | * |
| 818.6271_10.414 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.341349 | 23 | 0.0000201 | **** |
| 819.6141_8.816 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.279463 | 23 | 0.0000234 | **** |
| 819.6141_9.024 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.393267 | 23 | 0.0002110 | *** |
| 820.4801_7.866 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.549551 | 23 | 0.0017100 | ** |
| 820.627_10.414 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.803031 | 23 | 0.0009160 | *** |
| 821.6139_9.026 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.291687 | 23 | 0.0000227 | **** |
| 822.5279_5.875 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.287061 | 23 | 0.0317000 | * |
| 822.5774_8.941 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.138827 | 23 | 0.0003980 | *** |
| 823.6294_9.979 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.012376 | 23 | 0.0005460 | *** |
| 824.5433_5.925 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.026049 | 23 | 0.0060100 | ** |
| 824.5771_8.938 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.416886 | 23 | 0.0001990 | *** |
| 824.5789_7.841 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.420025 | 23 | 0.0000002 | **** |
| 826.5606_6.953 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.202727 | 23 | 0.0039500 | ** |
| 827.6272_7.905 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.361103 | 23 | 0.0000017 | **** |
| 828.5668_8.612 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.243486 | 23 | 0.0035900 | ** |
| 828.5759_7.19 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.793244 | 23 | 0.0009390 | *** |
| 828.5759_7.339 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.316699 | 23 | 0.0298000 | * |
| 828.6559_10.414 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.139645 | 23 | 0.0003980 | *** |
| 829.6431_8.816 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.916073 | 23 | 0.0006930 | *** |
| 829.6432_9.024 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.889329 | 23 | 0.0000614 | **** |
| 830.5815_7.188 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.701337 | 23 | 0.0011800 | ** |
| 831.6576_9.28 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.586808 | 23 | 0.0000110 | **** |
| 831.659_9.98 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.610630 | 23 | 0.0001230 | *** |
| 832.6067_8.94 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.470206 | 23 | 0.0020700 | ** |
| 832.6428_10.551 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.586830 | 23 | 0.0165000 | * |
| 833.5544_7.557 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.973553 | 23 | 0.0068000 | ** |
| 833.6294_9.395 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.143266 | 23 | 0.0000327 | **** |
| 834.5643_7.242 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.121127 | 23 | 0.0004160 | *** |
| 835.5322_8.336 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.594273 | 23 | 0.0000108 | **** |
| 836.5434_6.295 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.920817 | 23 | 0.0076900 | ** |
| 836.5799_7.605 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.967345 | 23 | 0.0069000 | ** |
| 837.6571_10.552 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.419456 | 23 | 0.0023500 | ** |
| 838.5955_8.279 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.303126 | 23 | 0.0031100 | ** |
| 838.5956_8.612 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.747623 | 23 | 0.0010500 | ** |
| 840.5304_6.407 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.255831 | 23 | 0.0002980 | *** |
| 840.5749_7.321 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.007795 | 23 | 0.0005520 | *** |
| 840.5749_7.471 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.173804 | 23 | 0.0000027 | **** |
| 840.6112_8.445 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.854226 | 23 | 0.0089800 | ** |
| 841.5661_6.75 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.947521 | 23 | 0.0006410 | *** |
| 841.6406_8.452 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.937106 | 23 | 0.0000047 | **** |
| 842.5306_8.773 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.798642 | 23 | 0.0102000 | * |
| 842.5905_7.891 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.270597 | 23 | 0.0000239 | **** |
| 843.5493_6.926 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.481248 | 23 | 0.0208000 | * |
| 843.5839_7.561 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.410784 | 23 | 0.0243000 | * |
| 843.6586_9.396 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.449425 | 23 | 0.0000014 | **** |
| 844.562_8.02 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.449329 | 23 | 0.0021800 | ** |
| 846.5617_8.018 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.789544 | 23 | 0.0104000 | * |
| 847.6304_9.032 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.122384 | 23 | 0.0047900 | ** |
| 847.6455_10.298 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.391609 | 23 | 0.0002120 | *** |
| 848.5114_9.046 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.994982 | 23 | 0.0005700 | *** |
| 848.577_8.458 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.656046 | 23 | 0.0141000 | * |
| 849.6458_10.294 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.329912 | 23 | 0.0289000 | * |
| 849.6462_9.988 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.875267 | 23 | 0.0007660 | *** |
| 849.6616_10.589 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.030695 | 23 | 0.0005210 | *** |
| 850.5595_6.407 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.911912 | 23 | 0.0007000 | *** |
| 851.6615_10.59 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.776627 | 23 | 0.0009780 | *** |
| 853.619_10.659 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.096100 | 23 | 0.0000368 | **** |
| 853.6428_8.12 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.617053 | 23 | 0.0000001 | **** |
| 854.5519_7.576 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.846028 | 23 | 0.0000684 | **** |
| 854.5906_7.701 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.179896 | 23 | 0.0003600 | *** |
| 854.5918_8.021 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.994043 | 23 | 0.0005710 | *** |
| 854.6714_10.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.500019 | 23 | 0.0019300 | ** |
| 855.59_8.755 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.909666 | 23 | 0.0007040 | *** |
| 855.6591_9.033 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.526102 | 23 | 0.0001520 | *** |
| 856.5377_7.769 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.512403 | 23 | 0.0195000 | * |
| 856.6051_8.21 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.359549 | 23 | 0.0272000 | * |
| 856.6871_10.641 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.058732 | 23 | 0.0004860 | *** |
| 857.5409_7.768 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.214898 | 23 | 0.0369000 | * |
| 857.6737_9.513 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.688097 | 23 | 0.0000001 | **** |
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| 857.6749_9.986 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.064599 | 23 | 0.0004790 | *** |
| 859.6907_10.59 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.341753 | 23 | 0.0002400 | *** |
| 860.6919_10.384 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.827954 | 23 | 0.0000715 | **** |
| 861.5449_6.542 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.361042 | 23 | 0.0271000 | * |
| 862.5261_7.47 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.463672 | 23 | 0.0001770 | *** |
| 862.527_7.471 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.396469 | 23 | 0.0000175 | **** |
| 862.5335_7.761 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.285278 | 23 | 0.0032400 | ** |
| 862.6535_10.583 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.344010 | 23 | 0.0028200 | ** |
| 862.664_10.445 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.098728 | 23 | 0.0470000 | * |
| 863.5644_9.582 | rel_abund_log2 | b1 | b3 | 24 | 24 | 8.571089 | 23 | 0.0000000 | **** |
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| 864.5747_7.263 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.530838 | 23 | 0.0001500 | *** |
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| 866.6268_8.879 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.523475 | 23 | 0.0190000 | * |
| 867.5493_6.755 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.594047 | 23 | 0.0015300 | ** |
| 868.5303_7.476 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.165113 | 23 | 0.0000310 | **** |
| 870.5618_7.814 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.772769 | 23 | 0.0009870 | *** |
| 871.5805_8.017 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.065450 | 23 | 0.0054800 | ** |
| 871.6147_8.768 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.827734 | 23 | 0.0008620 | *** |
| 871.6891_10.337 | rel_abund_log2 | b1 | b3 | 24 | 24 | -8.679015 | 23 | 0.0000000 | **** |
| 872.5626_7.769 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.230233 | 23 | 0.0000264 | **** |
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| 872.6815_10.583 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.650773 | 23 | 0.0013300 | ** |
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| 876.5748_6.674 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.894907 | 23 | 0.0081700 | ** |
| 878.5903_7.305 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.264773 | 23 | 0.0000242 | **** |
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| 879.5263_6.489 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.945916 | 23 | 0.0072500 | ** |
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| 881.6055_9.023 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.164691 | 23 | 0.0003730 | *** |
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| 883.6211_9.976 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.957314 | 23 | 0.0000519 | **** |
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| 887.5991_9.03 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.062154 | 23 | 0.0000035 | **** |
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| 889.617_9.974 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.770904 | 23 | 0.0009920 | *** |
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| 907.6211_9.035 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.797835 | 23 | 0.0102000 | * |
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| 909.6366_10.295 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.832321 | 23 | 0.0008520 | *** |
| 909.6368_9.99 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.206949 | 23 | 0.0003360 | *** |
| 911.6461_9.397 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.245416 | 23 | 0.0003050 | *** |
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| 913.6613_10.438 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.396641 | 23 | 0.0251000 | * |
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| 915.6327_10.293 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.977629 | 23 | 0.0005950 | *** |
| 917.5422_7.049 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.645195 | 23 | 0.0145000 | * |
| 917.6329_9.989 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.344524 | 23 | 0.0028100 | ** |
| 917.633_10.292 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.352180 | 23 | 0.0276000 | * |
| 917.6482_10.589 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.952557 | 23 | 0.0071400 | ** |
| 918.5466_6.408 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.883008 | 23 | 0.0084000 | ** |
| 918.5543_7.768 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.396768 | 23 | 0.0002090 | *** |
| 918.5546_7.77 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.168979 | 23 | 0.0042800 | ** |
| 919.2277_0.616 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.057019 | 23 | 0.0004880 | *** |
| 919.6483_10.59 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.139708 | 23 | 0.0432000 | * |
| 920.6735_10.31 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.052016 | 23 | 0.0000036 | **** |
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| 922.5774_7.695 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.437800 | 23 | 0.0022400 | ** |
| 922.5781_8.016 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.552545 | 23 | 0.0017000 | ** |
| 923.646_9.035 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.202579 | 23 | 0.0003400 | *** |
| 923.6524_10.353 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.423635 | 23 | 0.0000015 | **** |
| 924.5935_8.459 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.324461 | 23 | 0.0293000 | * |
| 925.6617_9.988 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.309254 | 23 | 0.0002600 | *** |
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| 93.0344_0.646 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.075428 | 23 | 0.0053500 | ** |
| 930.5504_7.815 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.587048 | 23 | 0.0015600 | ** |
| 931.5514_9.582 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.026738 | 23 | 0.0000004 | **** |
| 931.6162_9.992 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.384761 | 23 | 0.0257000 | * |
| 931.6162_9.999 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.013496 | 23 | 0.0061900 | ** |
| 933.5855_8.781 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.177299 | 23 | 0.0042000 | ** |
| 934.5326_7.772 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.266415 | 23 | 0.0033900 | ** |
| 936.5439_7.803 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.627182 | 23 | 0.0151000 | * |
| 938.5513_8.018 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.166164 | 23 | 0.0409000 | * |
| 939.6769_10.335 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.354445 | 23 | 0.0000018 | **** |
| 940.5496_7.764 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.822051 | 23 | 0.0008740 | *** |
| 942.5548_7.339 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.355364 | 23 | 0.0274000 | * |
| 942.5549_7.193 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.155616 | 23 | 0.0418000 | * |
| 945.5095_6.552 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.735307 | 23 | 0.0118000 | * |
| 945.6385_9.975 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.917278 | 23 | 0.0000573 | **** |
| 946.5851_8.939 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.554640 | 23 | 0.0177000 | * |
| 948.5172_7.192 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.677992 | 23 | 0.0134000 | * |
| 950.6082_8.859 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.064704 | 23 | 0.0004790 | *** |
| 958.5324_7.191 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.979074 | 23 | 0.0005930 | *** |
| 959.6018_9.026 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.695640 | 23 | 0.0011900 | ** |
| 960.7412_11.038 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.385688 | 23 | 0.0002150 | *** |
| 961.6178_9.977 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.383217 | 23 | 0.0025600 | ** |
| 964.5495_7.339 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.276145 | 23 | 0.0325000 | * |
| 967.6322_9.976 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.411283 | 23 | 0.0002020 | *** |
| 968.5707_8.019 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.350355 | 23 | 0.0277000 | * |
| 968.581_8.937 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.835836 | 23 | 0.0008450 | *** |
| 969.6384_9.033 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.549003 | 23 | 0.0179000 | * |
| 970.5858_8.459 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.554209 | 23 | 0.0177000 | * |
| 970.6153_10.616 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.201530 | 23 | 0.0380000 | * |
| 971.6542_9.988 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.184361 | 23 | 0.0003560 | *** |
| 976.5477_8.457 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.382561 | 23 | 0.0025600 | ** |
| 983.6184_9.976 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.397506 | 23 | 0.0002090 | *** |
| 983.6185_9.993 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.842628 | 23 | 0.0008310 | *** |
| 985.616_9.034 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.264112 | 23 | 0.0034100 | ** |
| 986.5637_8.459 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.528186 | 23 | 0.0188000 | * |
| 987.6318_9.987 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.493884 | 23 | 0.0019600 | ** |
| 991.6331_9.034 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.357923 | 23 | 0.0273000 | * |
| 992.5806_8.459 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.090919 | 23 | 0.0051600 | ** |
| 993.6487_10.29 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.469618 | 23 | 0.0020800 | ** |
| 993.6488_9.986 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.059540 | 23 | 0.0004850 | *** |
## [1] 388
# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_tomANDctrl <- full_join(tomato_t.test_paired_sig, ctrl_t.test_paired_sig,
by = "mz_rt",
suffix = c(".tom", ".ctrl"))
# number features in full list
nrow(fulljoin_t.test_tomANDctrl)## [1] 441
Let’s try and remove features significant due to the background diet
sig_paired_tom_rmBG <- fulljoin_t.test_tomANDctrl %>%
# add a column to account for direction
mutate(sign = statistic.tom * statistic.ctrl) %>%
# replace NAs in the sign column with 0
mutate(sign = replace_na(sign, 0)) %>%
# replace NAs in the statistic.ctrl column to 0
mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
# filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
filter((sign < 0 | statistic.ctrl == 0))
# number of features in new list without bg diet effect
nrow(sig_paired_tom_rmBG)## [1] 219
How many BG-diet-related features did we remove from the list of significant beta features?
## [1] 169
Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)
# select only features from paired list that have a match in ANOVA list
tom_sig_ANOVA_overlap_paired <- inner_join(sig_paired_tom_rmBG,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# features overlapping with sig ANOVA
unique(tom_sig_ANOVA_overlap_paired$mz_rt)## [1] "1002.5196_7.769" "1561.2028_9.038" "244.908_0.706" "526.3144_2.683"
## [5] "560.2269_6.891" "674.6039_10.834" "682.6332_10.835" "705.5178_5.308"
## [9] "721.549_6" "736.5275_8.432" "764.5798_8.343" "800.5357_7.469"
## [13] "824.5771_8.938" "847.6455_10.298" "848.5114_9.046" "853.619_10.659"
## [17] "857.6749_9.986" "862.5335_7.761" "863.5644_9.582" "868.5303_7.476"
## [21] "887.5991_9.03" "905.6028_9.99" "907.6211_9.035" "909.6368_9.99"
## [25] "915.6327_10.293" "917.6329_9.989" "940.5496_7.764" "945.6385_9.975"
## [29] "969.6384_9.033" "983.6184_9.976" "983.6185_9.993" "987.6318_9.987"
paired_tomato_for_mummichog <- tomato_t.test_paired %>%
dplyr::select(mz_rt,
p,
statistic) %>%
separate(col = mz_rt,
into = c("m/z", "rt"),
sep = "_") %>%
rename("p-value" = "p") %>%
rename("t-score" = "statistic")
write_csv(paired_red_for_mummichog,
"for mummichog analysis/t-test-res-tomato-paired.csv")Here, I will compare control to each tomato treatment individually, and then tomato treatments against each other. I will also compare tomato to control. I am using the log transformed values of rel abundance since parametric tests assume normality.
# run t-test
red_v_ctrl_t.test <- df_for_stats %>%
filter(treatment %in% c("control", "red")) %>%
filter(period == "b3") %>%
dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ treatment,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_red_v_ctrl_t.test <- red_v_ctrl_t.test %>%
filter(p < 0.05)
kable(sig_red_v_ctrl_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 151.0261_0.612 | rel_abund_log2 | control | red | 11 | 12 | 2.338023 | 16.36047 | 0.03240 | * |
| 244.908_0.706 | rel_abund_log2 | control | red | 11 | 12 | -3.452634 | 20.96521 | 0.00239 | ** |
| 362.9406_0.501 | rel_abund_log2 | control | red | 11 | 12 | 2.415061 | 15.52626 | 0.02850 | * |
| 605.4051_5.422 | rel_abund_log2 | control | red | 11 | 12 | -2.400517 | 20.64957 | 0.02590 | * |
| 705.5178_5.308 | rel_abund_log2 | control | red | 11 | 12 | -2.121762 | 20.23735 | 0.04640 | * |
| 711.5035_5.685 | rel_abund_log2 | control | red | 11 | 12 | -2.513206 | 19.15979 | 0.02110 | * |
| 737.5362_6.543 | rel_abund_log2 | control | red | 11 | 12 | 2.409275 | 20.99867 | 0.02520 | * |
| 860.6369_10.19 | rel_abund_log2 | control | red | 11 | 12 | -2.107560 | 20.55172 | 0.04750 | * |
| 877.5224_6.543 | rel_abund_log2 | control | red | 11 | 12 | 2.370644 | 20.53763 | 0.02760 | * |
| 880.6062_8.116 | rel_abund_log2 | control | red | 11 | 12 | 2.088743 | 20.83942 | 0.04920 | * |
| 890.5493_8.144 | rel_abund_log2 | control | red | 11 | 12 | -2.742924 | 20.97983 | 0.01220 | * |
| 895.6025_10.452 | rel_abund_log2 | control | red | 11 | 12 | -2.654041 | 20.99974 | 0.01480 | * |
| 895.6211_9.665 | rel_abund_log2 | control | red | 11 | 12 | 2.163212 | 20.79662 | 0.04230 | * |
| 929.5358_8.639 | rel_abund_log2 | control | red | 11 | 12 | -2.233441 | 20.71595 | 0.03670 | * |
| 931.6164_10.288 | rel_abund_log2 | control | red | 11 | 12 | 2.453375 | 20.32427 | 0.02330 | * |
| 945.5095_6.552 | rel_abund_log2 | control | red | 11 | 12 | 2.145392 | 16.42973 | 0.04720 | * |
| 945.6385_9.975 | rel_abund_log2 | control | red | 11 | 12 | 2.527456 | 20.03467 | 0.02000 | * |
## [1] 17
Keep sig features in unpaired t-test that have a match in sig ANOVA
sig_overlap_ctrl_red <- inner_join(sig_red_v_ctrl_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
unique(sig_overlap_ctrl_red$mz_rt)## [1] "244.908_0.706" "705.5178_5.308" "711.5035_5.685" "890.5493_8.144"
## [5] "895.6211_9.665" "945.6385_9.975"
# run t-tests
beta_v_ctrl_t.test <- df_for_stats %>%
filter(treatment %in% c("control" , "beta"),
period == "b3") %>%
dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ treatment,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_beta_v_ctrl_t.test <- beta_v_ctrl_t.test %>%
filter(p < 0.05)
kable(sig_beta_v_ctrl_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1271.7465_5.862 | rel_abund_log2 | beta | control | 12 | 11 | -2.602354 | 16.99728 | 0.01860 | * |
| 1440.1025_6.541 | rel_abund_log2 | beta | control | 12 | 11 | -2.498830 | 19.70687 | 0.02140 | * |
| 1450.1318_6.54 | rel_abund_log2 | beta | control | 12 | 11 | -2.261593 | 17.61030 | 0.03660 | * |
| 1502.0921_6.54 | rel_abund_log2 | beta | control | 12 | 11 | -2.601254 | 20.35857 | 0.01690 | * |
| 1518.1184_6.541 | rel_abund_log2 | beta | control | 12 | 11 | -2.642154 | 18.11451 | 0.01650 | * |
| 1612.0825_7.034 | rel_abund_log2 | beta | control | 12 | 11 | -2.578554 | 20.81114 | 0.01760 | * |
| 1644.3337_9.984 | rel_abund_log2 | beta | control | 12 | 11 | -3.083046 | 18.75198 | 0.00619 | ** |
| 1646.3475_10.439 | rel_abund_log2 | beta | control | 12 | 11 | -2.189729 | 13.27898 | 0.04700 | * |
| 445.3316_4.28 | rel_abund_log2 | beta | control | 12 | 11 | -2.634209 | 20.98463 | 0.01550 | * |
| 557.4566_6.309 | rel_abund_log2 | beta | control | 12 | 11 | -2.178572 | 17.72385 | 0.04310 | * |
| 575.467_4.662 | rel_abund_log2 | beta | control | 12 | 11 | -2.327719 | 19.80703 | 0.03060 | * |
| 654.31_3.076 | rel_abund_log2 | beta | control | 12 | 11 | 2.867323 | 20.75135 | 0.00929 | ** |
| 737.5193_5.846 | rel_abund_log2 | beta | control | 12 | 11 | -2.240287 | 20.78231 | 0.03610 | * |
| 737.5362_6.543 | rel_abund_log2 | beta | control | 12 | 11 | -2.614394 | 16.17349 | 0.01870 | * |
| 745.5498_5.846 | rel_abund_log2 | beta | control | 12 | 11 | -2.159582 | 20.62657 | 0.04270 | * |
| 764.5548_6.543 | rel_abund_log2 | beta | control | 12 | 11 | -3.122747 | 15.46343 | 0.00678 | ** |
| 767.5664_7.562 | rel_abund_log2 | beta | control | 12 | 11 | -2.527314 | 20.00745 | 0.02000 | * |
| 797.5117_5.847 | rel_abund_log2 | beta | control | 12 | 11 | -2.608931 | 20.73450 | 0.01650 | * |
| 803.5076_5.847 | rel_abund_log2 | beta | control | 12 | 11 | -2.145926 | 20.86749 | 0.04380 | * |
| 819.6141_9.024 | rel_abund_log2 | beta | control | 12 | 11 | -2.315440 | 20.88308 | 0.03080 | * |
| 821.565_7.768 | rel_abund_log2 | beta | control | 12 | 11 | 2.410504 | 20.46941 | 0.02540 | * |
| 821.6152_8.817 | rel_abund_log2 | beta | control | 12 | 11 | -2.242101 | 20.54913 | 0.03610 | * |
| 829.6432_9.024 | rel_abund_log2 | beta | control | 12 | 11 | -2.109984 | 20.99862 | 0.04700 | * |
| 833.6297_9.665 | rel_abund_log2 | beta | control | 12 | 11 | -2.412993 | 20.99947 | 0.02500 | * |
| 843.6587_9.666 | rel_abund_log2 | beta | control | 12 | 11 | -2.531978 | 20.89053 | 0.01940 | * |
| 847.6455_10.298 | rel_abund_log2 | beta | control | 12 | 11 | -2.409479 | 20.95110 | 0.02530 | * |
| 853.6428_8.12 | rel_abund_log2 | beta | control | 12 | 11 | -2.356496 | 20.26352 | 0.02860 | * |
| 855.6591_9.033 | rel_abund_log2 | beta | control | 12 | 11 | -2.187249 | 18.52780 | 0.04180 | * |
| 857.6746_10.294 | rel_abund_log2 | beta | control | 12 | 11 | -2.186688 | 20.47124 | 0.04050 | * |
| 869.598_7.834 | rel_abund_log2 | beta | control | 12 | 11 | -2.265410 | 20.94333 | 0.03420 | * |
| 877.5224_6.543 | rel_abund_log2 | beta | control | 12 | 11 | -2.086665 | 20.96393 | 0.04930 | * |
| 886.5199_7.016 | rel_abund_log2 | beta | control | 12 | 11 | 2.438518 | 18.87384 | 0.02480 | * |
| 890.5493_8.144 | rel_abund_log2 | beta | control | 12 | 11 | 2.103000 | 20.71634 | 0.04790 | * |
| 895.6211_9.665 | rel_abund_log2 | beta | control | 12 | 11 | -2.483772 | 20.49984 | 0.02170 | * |
| 896.6737_10.623 | rel_abund_log2 | beta | control | 12 | 11 | -2.117449 | 19.00880 | 0.04760 | * |
| 903.5878_9.033 | rel_abund_log2 | beta | control | 12 | 11 | -2.870222 | 19.78432 | 0.00953 | ** |
| 904.599_5.836 | rel_abund_log2 | beta | control | 12 | 11 | -2.449384 | 20.44569 | 0.02340 | * |
| 907.6211_9.035 | rel_abund_log2 | beta | control | 12 | 11 | -2.927436 | 16.99932 | 0.00940 | ** |
| 911.6459_9.666 | rel_abund_log2 | beta | control | 12 | 11 | -2.515074 | 20.74477 | 0.02020 | * |
| 913.6162_9.035 | rel_abund_log2 | beta | control | 12 | 11 | -2.368208 | 20.03066 | 0.02800 | * |
| 917.633_10.292 | rel_abund_log2 | beta | control | 12 | 11 | -2.170774 | 20.43166 | 0.04190 | * |
| 923.646_9.035 | rel_abund_log2 | beta | control | 12 | 11 | -2.772763 | 20.76640 | 0.01150 | * |
| 931.6164_10.288 | rel_abund_log2 | beta | control | 12 | 11 | -2.372446 | 19.13541 | 0.02830 | * |
| 945.6385_9.975 | rel_abund_log2 | beta | control | 12 | 11 | -2.665153 | 19.65665 | 0.01500 | * |
| 950.5333_8.144 | rel_abund_log2 | beta | control | 12 | 11 | 2.392149 | 17.30117 | 0.02840 | * |
| 967.6322_9.976 | rel_abund_log2 | beta | control | 12 | 11 | -2.774084 | 19.40881 | 0.01190 | * |
| 969.6092_10.587 | rel_abund_log2 | beta | control | 12 | 11 | 2.401877 | 20.33160 | 0.02600 | * |
| 985.616_9.034 | rel_abund_log2 | beta | control | 12 | 11 | -2.247727 | 20.07430 | 0.03600 | * |
| 991.6331_9.034 | rel_abund_log2 | beta | control | 12 | 11 | -2.236592 | 18.15637 | 0.03810 | * |
## [1] 49
Keep sig features in t-test that have a match in sig ANOVA
sig_overlap_ctrl_beta <- inner_join(sig_beta_v_ctrl_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# which features overlap?
unique(sig_overlap_ctrl_beta$mz_rt)## [1] "1271.7465_5.862" "1646.3475_10.439" "557.4566_6.309" "575.467_4.662"
## [5] "764.5548_6.543" "767.5664_7.562" "797.5117_5.847" "819.6141_9.024"
## [9] "829.6432_9.024" "833.6297_9.665" "843.6587_9.666" "847.6455_10.298"
## [13] "853.6428_8.12" "855.6591_9.033" "857.6746_10.294" "869.598_7.834"
## [17] "886.5199_7.016" "890.5493_8.144" "895.6211_9.665" "903.5878_9.033"
## [21] "907.6211_9.035" "911.6459_9.666" "913.6162_9.035" "923.646_9.035"
## [25] "945.6385_9.975" "985.616_9.034"
ctrl_v_beta_for_mummichog <- beta_v_ctrl_t.test %>%
dplyr::select(mz_rt,
p,
statistic) %>%
separate(col = mz_rt,
into = c("m/z", "rt"),
sep = "_") %>%
rename("p-value" = "p") %>%
rename("t-score" = "statistic")
write_csv(ctrl_v_beta_for_mummichog,
"for mummichog analysis/t-test-res-ctrl-v-beta.csv")# run t-tests
beta_v_red_t.test <- df_for_stats %>%
filter(treatment %in% c("beta", "red"),
period == "b3") %>%
dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ treatment,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_beta_v_red_t.test <- beta_v_red_t.test %>%
filter(p < 0.05)
kable(sig_beta_v_red_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 244.908_0.706 | rel_abund_log2 | beta | red | 12 | 12 | -2.099288 | 21.52667 | 0.04780 | * |
| 297.1527_2.281 | rel_abund_log2 | beta | red | 12 | 12 | 2.770830 | 21.98423 | 0.01120 | * |
| 311.1683_2.682 | rel_abund_log2 | beta | red | 12 | 12 | 2.669852 | 19.44820 | 0.01490 | * |
| 362.9406_0.501 | rel_abund_log2 | beta | red | 12 | 12 | 3.560133 | 18.97481 | 0.00209 | ** |
| 473.3629_4.632 | rel_abund_log2 | beta | red | 12 | 12 | -2.397707 | 21.54749 | 0.02560 | * |
| 532.2989_3.075 | rel_abund_log2 | beta | red | 12 | 12 | 2.946249 | 16.22111 | 0.00938 | ** |
| 557.4566_6.309 | rel_abund_log2 | beta | red | 12 | 12 | -2.889624 | 19.50467 | 0.00922 | ** |
| 559.4719_6.867 | rel_abund_log2 | beta | red | 12 | 12 | -2.371316 | 18.25286 | 0.02890 | * |
| 591.462_4.314 | rel_abund_log2 | beta | red | 12 | 12 | -2.419627 | 21.99982 | 0.02420 | * |
| 654.31_3.076 | rel_abund_log2 | beta | red | 12 | 12 | 2.679382 | 21.32549 | 0.01390 | * |
| 711.5035_5.685 | rel_abund_log2 | beta | red | 12 | 12 | -2.076854 | 21.94549 | 0.04970 | * |
| 797.5117_5.847 | rel_abund_log2 | beta | red | 12 | 12 | -2.196075 | 21.92973 | 0.03900 | * |
| 818.6271_10.414 | rel_abund_log2 | beta | red | 12 | 12 | -2.330254 | 21.99011 | 0.02940 | * |
| 820.5155_3.079 | rel_abund_log2 | beta | red | 12 | 12 | 3.050123 | 19.73669 | 0.00639 | ** |
| 830.5005_2.723 | rel_abund_log2 | beta | red | 12 | 12 | 2.198696 | 15.31911 | 0.04370 | * |
| 840.6112_8.445 | rel_abund_log2 | beta | red | 12 | 12 | -2.925405 | 19.19274 | 0.00862 | ** |
| 849.4911_5.685 | rel_abund_log2 | beta | red | 12 | 12 | -2.299673 | 21.95091 | 0.03140 | * |
| 853.619_10.659 | rel_abund_log2 | beta | red | 12 | 12 | -2.104137 | 21.98626 | 0.04700 | * |
| 860.6369_10.19 | rel_abund_log2 | beta | red | 12 | 12 | -2.137842 | 21.45621 | 0.04420 | * |
| 878.584_5.681 | rel_abund_log2 | beta | red | 12 | 12 | -2.223055 | 21.24129 | 0.03720 | * |
| 903.5878_9.033 | rel_abund_log2 | beta | red | 12 | 12 | -3.393211 | 18.39625 | 0.00316 | ** |
## [1] 21
Keep sig features in t-test that have a match in sig ANOVA
sig_overlap_beta_red <- inner_join(sig_beta_v_red_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# which features overlap?
unique(sig_overlap_beta_red$mz_rt)## [1] "244.908_0.706" "557.4566_6.309" "559.4719_6.867" "591.462_4.314"
## [5] "711.5035_5.685" "797.5117_5.847" "818.6271_10.414" "853.619_10.659"
## [9] "903.5878_9.033"
# run t-tests
tom_v_ctrl_t.test <- df_for_stats %>%
filter(tomato_or_control %in% c("control", "tomato"),
period == "b3") %>%
dplyr::select(subject, tomato_or_control, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ tomato_or_control,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_tom_v_ctrl_t.test <- tom_v_ctrl_t.test %>%
filter(p < 0.05)
kable(sig_tom_v_ctrl_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1440.1025_6.541 | rel_abund_log2 | control | tomato | 11 | 24 | 2.697449 | 26.30317 | 0.01200 | * |
| 1450.1318_6.54 | rel_abund_log2 | control | tomato | 11 | 24 | 2.162725 | 28.17691 | 0.03920 | * |
| 1502.0921_6.54 | rel_abund_log2 | control | tomato | 11 | 24 | 2.254239 | 24.47013 | 0.03340 | * |
| 1518.1184_6.541 | rel_abund_log2 | control | tomato | 11 | 24 | 2.559290 | 29.02690 | 0.01600 | * |
| 1644.3337_9.984 | rel_abund_log2 | control | tomato | 11 | 24 | 3.122548 | 27.03693 | 0.00424 | ** |
| 1646.3475_10.439 | rel_abund_log2 | control | tomato | 11 | 24 | 2.107761 | 32.56337 | 0.04280 | * |
| 244.908_0.706 | rel_abund_log2 | control | tomato | 11 | 24 | -2.849128 | 22.07836 | 0.00931 | ** |
| 526.3144_2.683 | rel_abund_log2 | control | tomato | 11 | 24 | -2.114205 | 24.24561 | 0.04500 | * |
| 605.4051_5.422 | rel_abund_log2 | control | tomato | 11 | 24 | -2.448664 | 30.43772 | 0.02030 | * |
| 737.5362_6.543 | rel_abund_log2 | control | tomato | 11 | 24 | 3.093542 | 30.22575 | 0.00423 | ** |
| 745.5498_5.846 | rel_abund_log2 | control | tomato | 11 | 24 | 2.072676 | 22.54078 | 0.04980 | * |
| 764.5548_6.543 | rel_abund_log2 | control | tomato | 11 | 24 | 2.799299 | 32.96766 | 0.00849 | ** |
| 767.5664_7.562 | rel_abund_log2 | control | tomato | 11 | 24 | 2.470170 | 25.78700 | 0.02040 | * |
| 775.5965_7.563 | rel_abund_log2 | control | tomato | 11 | 24 | 2.107058 | 31.71271 | 0.04310 | * |
| 788.5437_6.549 | rel_abund_log2 | control | tomato | 11 | 24 | -2.387040 | 30.29985 | 0.02340 | * |
| 803.5076_5.847 | rel_abund_log2 | control | tomato | 11 | 24 | 2.128987 | 19.93580 | 0.04590 | * |
| 819.6141_9.024 | rel_abund_log2 | control | tomato | 11 | 24 | 2.335152 | 20.56272 | 0.02980 | * |
| 821.565_7.768 | rel_abund_log2 | control | tomato | 11 | 24 | -2.514823 | 17.29324 | 0.02210 | * |
| 821.6139_9.026 | rel_abund_log2 | control | tomato | 11 | 24 | 2.170798 | 18.09512 | 0.04350 | * |
| 829.6432_9.024 | rel_abund_log2 | control | tomato | 11 | 24 | 2.198822 | 20.17973 | 0.03970 | * |
| 833.6297_9.665 | rel_abund_log2 | control | tomato | 11 | 24 | 2.494656 | 23.32964 | 0.02010 | * |
| 835.6457_10.444 | rel_abund_log2 | control | tomato | 11 | 24 | 2.294734 | 32.89860 | 0.02830 | * |
| 843.6587_9.666 | rel_abund_log2 | control | tomato | 11 | 24 | 2.165548 | 23.80584 | 0.04060 | * |
| 847.6455_10.298 | rel_abund_log2 | control | tomato | 11 | 24 | 2.492918 | 25.05938 | 0.01960 | * |
| 855.6591_9.033 | rel_abund_log2 | control | tomato | 11 | 24 | 2.389511 | 26.77448 | 0.02420 | * |
| 857.6746_10.294 | rel_abund_log2 | control | tomato | 11 | 24 | 2.410083 | 26.40524 | 0.02320 | * |
| 869.598_7.834 | rel_abund_log2 | control | tomato | 11 | 24 | 2.111475 | 19.29221 | 0.04800 | * |
| 877.5224_6.543 | rel_abund_log2 | control | tomato | 11 | 24 | 2.644829 | 22.23392 | 0.01470 | * |
| 886.5199_7.016 | rel_abund_log2 | control | tomato | 11 | 24 | -2.643888 | 27.67046 | 0.01330 | * |
| 890.5493_8.144 | rel_abund_log2 | control | tomato | 11 | 24 | -2.821312 | 20.34803 | 0.01040 | * |
| 895.6211_9.665 | rel_abund_log2 | control | tomato | 11 | 24 | 2.790066 | 23.61123 | 0.01030 | * |
| 907.6211_9.035 | rel_abund_log2 | control | tomato | 11 | 24 | 2.873048 | 30.43628 | 0.00734 | ** |
| 911.6459_9.666 | rel_abund_log2 | control | tomato | 11 | 24 | 2.199363 | 21.02825 | 0.03920 | * |
| 913.6162_9.035 | rel_abund_log2 | control | tomato | 11 | 24 | 2.421647 | 22.29324 | 0.02400 | * |
| 915.6327_10.293 | rel_abund_log2 | control | tomato | 11 | 24 | 2.394575 | 31.08177 | 0.02290 | * |
| 923.646_9.035 | rel_abund_log2 | control | tomato | 11 | 24 | 2.668043 | 21.54311 | 0.01420 | * |
| 924.518_7.766 | rel_abund_log2 | control | tomato | 11 | 24 | -2.080283 | 26.40791 | 0.04730 | * |
| 931.6164_10.288 | rel_abund_log2 | control | tomato | 11 | 24 | 2.962534 | 26.56891 | 0.00636 | ** |
| 945.5095_6.552 | rel_abund_log2 | control | tomato | 11 | 24 | 2.716900 | 32.63198 | 0.01050 | * |
| 945.6385_9.975 | rel_abund_log2 | control | tomato | 11 | 24 | 3.060858 | 22.80094 | 0.00557 | ** |
| 950.5333_8.144 | rel_abund_log2 | control | tomato | 11 | 24 | -2.051392 | 29.80883 | 0.04910 | * |
| 967.6322_9.976 | rel_abund_log2 | control | tomato | 11 | 24 | 2.919161 | 26.49412 | 0.00708 | ** |
| 969.6092_10.587 | rel_abund_log2 | control | tomato | 11 | 24 | -2.231956 | 21.04607 | 0.03660 | * |
| 971.6542_9.988 | rel_abund_log2 | control | tomato | 11 | 24 | 2.179708 | 20.40680 | 0.04120 | * |
| 985.616_9.034 | rel_abund_log2 | control | tomato | 11 | 24 | 2.495541 | 24.00961 | 0.01980 | * |
| 991.6331_9.034 | rel_abund_log2 | control | tomato | 11 | 24 | 2.050243 | 29.21562 | 0.04940 | * |
## [1] 46
Keep sig features in t-test that have a match in sig ANOVA
sig_overlap_tom_ctrl <- inner_join(sig_tom_v_ctrl_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# which features overlap?
unique(sig_overlap_tom_ctrl$mz_rt)## [1] "1646.3475_10.439" "244.908_0.706" "526.3144_2.683" "764.5548_6.543"
## [5] "767.5664_7.562" "819.6141_9.024" "821.6139_9.026" "829.6432_9.024"
## [9] "833.6297_9.665" "835.6457_10.444" "843.6587_9.666" "847.6455_10.298"
## [13] "855.6591_9.033" "857.6746_10.294" "869.598_7.834" "886.5199_7.016"
## [17] "890.5493_8.144" "895.6211_9.665" "907.6211_9.035" "911.6459_9.666"
## [21] "913.6162_9.035" "915.6327_10.293" "923.646_9.035" "924.518_7.766"
## [25] "945.6385_9.975" "971.6542_9.988" "985.616_9.034"
# go back to wide for stats df
df_for_stats_wide <- df_for_stats %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)
ANOVA_trtperiod_heatmap_data <- df_for_stats_wide %>%
filter(period == "b3") %>%
dplyr::select(sample,
all_of(trt_anova_sig$mz_rt)) %>%
column_to_rownames("sample")
head(ANOVA_trtperiod_heatmap_data, n=3)## 1334.2417_10.855 1335.245_10.854 1554.1226_7.773
## x5107_b3_beta_c18neg_28 11.89168 11.93301 13.11223
## x5109_b3_beta_c18neg_81 11.35384 11.39136 12.35018
## x5112_b3_beta_c18neg_56 11.66017 11.60905 12.46035
## 1561.2028_9.038 201.0226_0.688 244.908_0.706
## x5107_b3_beta_c18neg_28 12.38974 8.987843 14.28148
## x5109_b3_beta_c18neg_81 11.33230 10.583197 14.24202
## x5112_b3_beta_c18neg_56 12.17100 10.430688 14.90995
## 352.0856_0.687 437.0542_0.688 462.1763_0.655
## x5107_b3_beta_c18neg_28 12.19208 11.59417 12.12562
## x5109_b3_beta_c18neg_81 13.44411 13.69906 13.46653
## x5112_b3_beta_c18neg_56 12.33612 12.14682 12.06409
## 507.223_1.7 507.223_2.273 551.3582_4.841 557.457_6.865
## x5107_b3_beta_c18neg_28 15.82112 13.36222 15.04541 11.88666
## x5109_b3_beta_c18neg_81 16.10839 13.94255 14.21245 11.49448
## x5112_b3_beta_c18neg_56 15.89406 13.64799 14.45460 11.97055
## 559.4719_6.867 577.3738_4.918 579.3893_5.557
## x5107_b3_beta_c18neg_28 11.90796 13.87389 14.01704
## x5109_b3_beta_c18neg_81 11.98511 13.66888 13.95120
## x5112_b3_beta_c18neg_56 12.29566 13.54795 13.40897
## 591.3894_5.174 591.462_4.314 591.462_4.651
## x5107_b3_beta_c18neg_28 14.09410 12.10241 11.67833
## x5109_b3_beta_c18neg_81 13.69385 12.19859 11.83841
## x5112_b3_beta_c18neg_56 13.98307 12.07909 11.69268
## 684.6065_10.853 794.5464_7.772 818.6271_10.414
## x5107_b3_beta_c18neg_28 18.13677 16.37619 14.97830
## x5109_b3_beta_c18neg_81 17.86967 15.88000 14.50881
## x5112_b3_beta_c18neg_56 18.15119 16.18453 14.68936
## 819.6141_9.024 821.6139_9.026 827.6272_7.905
## x5107_b3_beta_c18neg_28 14.35328 13.00312 14.11301
## x5109_b3_beta_c18neg_81 13.86363 12.69708 13.59230
## x5112_b3_beta_c18neg_56 14.10297 12.95969 14.13292
## 829.6432_9.024 831.6576_9.28 841.6406_8.452
## x5107_b3_beta_c18neg_28 16.79052 12.46298 12.79270
## x5109_b3_beta_c18neg_81 16.20519 12.29325 12.47057
## x5112_b3_beta_c18neg_56 16.42180 12.76196 12.78832
## 843.6586_9.396 853.6428_8.12 857.6737_9.513
## x5107_b3_beta_c18neg_28 14.98839 13.31049 11.91099
## x5109_b3_beta_c18neg_81 14.43325 13.54001 11.49423
## x5112_b3_beta_c18neg_56 14.96732 13.58702 12.29627
## 863.5644_9.582 871.6891_10.337 878.5903_7.305
## x5107_b3_beta_c18neg_28 14.15278 14.94652 13.50403
## x5109_b3_beta_c18neg_81 13.57718 14.86260 13.29179
## x5112_b3_beta_c18neg_56 13.52384 15.42354 13.60690
## 881.6055_9.023 883.6211_9.976 887.5991_9.03
## x5107_b3_beta_c18neg_28 13.27430 13.67873 12.98071
## x5109_b3_beta_c18neg_81 12.90949 13.42718 12.51242
## x5112_b3_beta_c18neg_56 12.93948 13.42897 12.94346
## 931.5514_9.582 939.6769_10.335
## x5107_b3_beta_c18neg_28 11.86885 12.68854
## x5109_b3_beta_c18neg_81 11.07676 12.44487
## x5112_b3_beta_c18neg_56 11.36730 13.06843
# pull metadata
metadata_Heatmap <- metadata
# change treatment to factor
metadata_Heatmap$treatment <- as.factor(metadata_Heatmap$treatment)
# make it so that rownames in metadata match rownames from heatmap df
rownames(metadata_Heatmap) <- rownames(ANOVA_trtperiod_heatmap_data)
# create annotation rows for treatment and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_trt_row <- as.data.frame(rownames(metadata_Heatmap))
# pull trt column
anno_trt_row$treatment <- metadata_Heatmap$treatment
anno_trt_row$sex <- metadata_Heatmap$sex
# select trt
anno_trt_row <- anno_trt_row %>%
dplyr::select(treatment, sex)
# get rownames to match heatmap again
rownames(anno_trt_row) <- rownames(metadata_Heatmap)# create annotation colors
annotation_colors <- list(treatment = c("beta" = "orange",
"control" = "green",
"red" = "tomato"),
sex = c("M" = "burlywood",
"F" = "pink"))pheatmap(t(ANOVA_trtperiod_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = TRUE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-) \nShowing only post-intervention timepoints") # Without hierarchical clustering of samples (cols)
pheatmap(t(ANOVA_trtperiod_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = FALSE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in ANOVA across treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-) \nShowing only post-intervention timepoints") unpaired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(period == "b3") %>%
dplyr::select(sample,
all_of(sig_overlap_ctrl_beta$mz_rt),
all_of(sig_overlap_ctrl_red$mz_rt),
all_of(sig_overlap_tom_ctrl$mz_rt),
all_of(sig_overlap_beta_red$mz_rt)) %>%
column_to_rownames("sample")
head(unpaired_t.tests_heatmap_data,n = 3)## 1271.7465_5.862 1646.3475_10.439 557.4566_6.309
## x5107_b3_beta_c18neg_28 13.17066 11.59082 12.53121
## x5109_b3_beta_c18neg_81 12.69443 10.85874 12.40276
## x5112_b3_beta_c18neg_56 12.89604 11.55739 12.92316
## 575.467_4.662 764.5548_6.543 767.5664_7.562
## x5107_b3_beta_c18neg_28 11.09816 13.46956 13.17776
## x5109_b3_beta_c18neg_81 11.20832 13.17250 12.96589
## x5112_b3_beta_c18neg_56 10.81837 13.59083 12.86548
## 797.5117_5.847 819.6141_9.024 829.6432_9.024
## x5107_b3_beta_c18neg_28 13.91578 14.35328 16.79052
## x5109_b3_beta_c18neg_81 13.51247 13.86363 16.20519
## x5112_b3_beta_c18neg_56 13.63163 14.10297 16.42180
## 833.6297_9.665 843.6587_9.666 847.6455_10.298
## x5107_b3_beta_c18neg_28 13.78397 15.71573 15.55911
## x5109_b3_beta_c18neg_81 13.44514 15.19044 15.26548
## x5112_b3_beta_c18neg_56 13.63559 15.45538 15.33875
## 853.6428_8.12 855.6591_9.033 857.6746_10.294
## x5107_b3_beta_c18neg_28 13.31049 17.38560 17.24798
## x5109_b3_beta_c18neg_81 13.54001 16.73622 16.68291
## x5112_b3_beta_c18neg_56 13.58702 17.22952 16.89029
## 869.598_7.834 886.5199_7.016 890.5493_8.144
## x5107_b3_beta_c18neg_28 12.79628 12.77818 13.58262
## x5109_b3_beta_c18neg_81 12.62707 13.02092 13.32260
## x5112_b3_beta_c18neg_56 12.19584 12.90366 13.57013
## 895.6211_9.665 903.5878_9.033 907.6211_9.035
## x5107_b3_beta_c18neg_28 12.76520 12.38543 13.72623
## x5109_b3_beta_c18neg_81 12.22281 12.16643 13.40179
## x5112_b3_beta_c18neg_56 12.78374 12.40267 13.68643
## 911.6459_9.666 913.6162_9.035 923.646_9.035
## x5107_b3_beta_c18neg_28 13.62686 13.41607 14.85786
## x5109_b3_beta_c18neg_81 13.23490 13.14782 14.44927
## x5112_b3_beta_c18neg_56 13.29647 13.34383 14.79689
## 945.6385_9.975 985.616_9.034 244.908_0.706
## x5107_b3_beta_c18neg_28 12.33899 13.17591 14.28148
## x5109_b3_beta_c18neg_81 12.12268 12.58536 14.24202
## x5112_b3_beta_c18neg_56 12.11486 13.00376 14.90995
## 705.5178_5.308 711.5035_5.685 526.3144_2.683
## x5107_b3_beta_c18neg_28 12.77114 13.34210 14.23104
## x5109_b3_beta_c18neg_81 12.15313 12.85181 14.24883
## x5112_b3_beta_c18neg_56 12.91117 13.14473 14.13615
## 821.6139_9.026 835.6457_10.444 915.6327_10.293
## x5107_b3_beta_c18neg_28 13.00312 16.81563 13.85985
## x5109_b3_beta_c18neg_81 12.69708 16.53929 13.31377
## x5112_b3_beta_c18neg_56 12.95969 16.76968 13.65187
## 924.518_7.766 971.6542_9.988 559.4719_6.867
## x5107_b3_beta_c18neg_28 12.89559 12.88359 11.90796
## x5109_b3_beta_c18neg_81 13.11896 12.54816 11.98511
## x5112_b3_beta_c18neg_56 13.20034 12.81446 12.29566
## 591.462_4.314 818.6271_10.414 853.619_10.659
## x5107_b3_beta_c18neg_28 12.10241 14.97830 11.82644
## x5109_b3_beta_c18neg_81 12.19859 14.50881 11.56742
## x5112_b3_beta_c18neg_56 12.07909 14.68936 11.96410
pheatmap(t(unpaired_t.tests_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = TRUE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-)")# without clustering of cols
pheatmap(t(unpaired_t.tests_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = FALSE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-)")red_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(treatment == "red") %>%
dplyr::select(sample, period, treatment, sex,
all_of(red_sig_ANOVA_overlap_paired$mz_rt)) %>%
mutate_at("period", as.factor) %>%
column_to_rownames("sample")# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_red_row_paired <- as.data.frame(rownames(red_paired_t.tests_heatmap_data))
# pull period into a column
anno_red_row_paired$period <- red_paired_t.tests_heatmap_data$period
anno_red_row_paired$sex <- red_paired_t.tests_heatmap_data$sex
# select cols
anno_red_row_paired <- anno_red_row_paired %>%
dplyr::select(period, sex)
# get rownames to match heatmap again
rownames(anno_red_row_paired) <- rownames(red_paired_t.tests_heatmap_data)# create annotation colors
red_annotation_colors <- list(period = c("b1" = "darksalmon",
"b3" = "red"),
sex = c("M" = "aquamarine2",
"F" = "pink"))pheatmap(t(red_paired_t.tests_heatmap_data[,-c(1:3)]),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_red_row_paired,
annotation_colors = red_annotation_colors,
cluster_cols = TRUE,
show_rownames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 2,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of significant features pre- vs. post-High-Lyc paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (-)")beta_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(treatment == "beta") %>%
dplyr::select(sample, period, treatment, sex,
all_of(beta_sig_ANOVA_overlap_paired$mz_rt)) %>%
mutate_at("period", as.factor) %>%
column_to_rownames("sample")# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_beta_row_paired <- as.data.frame(rownames(beta_paired_t.tests_heatmap_data))
# pull period into a column
anno_beta_row_paired$period <- beta_paired_t.tests_heatmap_data$period
anno_beta_row_paired$sex <- beta_paired_t.tests_heatmap_data$sex
# select cols
anno_beta_row_paired <- anno_beta_row_paired %>%
dplyr::select(sex, period)
# get rownames to match heatmap again
rownames(anno_beta_row_paired) <- rownames(beta_paired_t.tests_heatmap_data)# create annotation colors
beta_annotation_colors <- list(period = c("b1" = "bisque",
"b3" = "darkorange"),
sex = c("M" = "aquamarine2",
"F" = "pink"))pheatmap(t(beta_paired_t.tests_heatmap_data[,-c(1:3)]),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_beta_row_paired,
annotation_colors = beta_annotation_colors,
cluster_cols = TRUE,
show_rownames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 10,
cutree_cols = 5,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of significant features pre- vs. post-High-beta paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")tom_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(treatment != "control") %>%
dplyr::select(sample, period, tomato_or_control, sex,
all_of(tom_sig_ANOVA_overlap_paired$mz_rt)) %>%
mutate_at("period", as.factor) %>%
column_to_rownames("sample")# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_tom_row_paired <- as.data.frame(rownames(tom_paired_t.tests_heatmap_data))
# pull period into a column
anno_tom_row_paired$period <- tom_paired_t.tests_heatmap_data$period
anno_tom_row_paired$sex <- tom_paired_t.tests_heatmap_data$sex
# select cols
anno_tom_row_paired <- anno_tom_row_paired %>%
dplyr::select(period, sex)
# get rownames to match heatmap again
rownames(anno_tom_row_paired) <- rownames(tom_paired_t.tests_heatmap_data)# create annotation colors
tom_annotation_colors <- list(period = c("b1" = "darksalmon",
"b3" = "tomato"),
sex = c("M" = "aquamarine2",
"F" = "pink"))pheatmap(t(tom_paired_t.tests_heatmap_data[,-c(1:3)]),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_tom_row_paired,
annotation_colors = tom_annotation_colors,
cluster_cols = TRUE,
show_rownames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 2,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of significant features pre- vs. post-Tomato paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")df_for_stats <- df_for_stats %>%
# add rel abund levels back since this got lost during drift correction
mutate(rel_abund = 2^(rel_abund_log2))How many features are significant (with background diet effect removed)
## [1] 219 20
Which features are significant in unpaired t test comparisons?
# select only significant features from pre v post tomato effect that have a matching key to significant features post tomato v. post control comparison
overall_tomato_effect <- semi_join(sig_paired_tom_rmBG,
sig_tom_v_ctrl_t.test,
by = "mz_rt")
dim(overall_tomato_effect)## [1] 9 20
(FC_tomato_effect <- df_for_stats %>%
filter(mz_rt %in% overall_tomato_effect$mz_rt) %>%
select(subject, treatment_period, mz_rt, rel_abund) %>%
group_by(mz_rt) %>%
pivot_wider(names_from = treatment_period,
values_from = rel_abund) %>%
mutate(control_FC = control_b3/control_b1,
beta_FC = beta_b3/beta_b1,
red_FC = red_b3/red_b1) %>%
summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
mean_beta_FC = mean(beta_FC, na.rm = TRUE),
mean_red_FC = mean(red_FC, na.rm = TRUE),
mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))## # A tibble: 9 × 7
## mz_rt mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 244.908_0.706 1.07 1.08 1.08 1.00
## 2 526.3144_2.683 1.15 1.23 1.09 1.07
## 3 847.6455_10.298 0.913 0.857 0.927 0.938
## 4 907.6211_9.035 1.10 1.12 1.11 1.01
## 5 915.6327_10.293 0.917 0.895 0.884 0.975
## 6 945.5095_6.552 1.05 0.941 0.965 0.898
## 7 945.6385_9.975 1.02 0.853 0.924 0.836
## 8 967.6322_9.976 0.980 0.873 0.903 0.891
## 9 991.6331_9.034 1.15 1.11 1.11 0.964
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>
# combine cluster and FC info
tom_effect_list <- left_join(FC_tomato_effect,
cluster_features,
by = "mz_rt") %>%
select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())# add in averages for each group + timepoint
tom_effect_list <-
left_join(tom_effect_list,
(df_for_stats %>%
group_by(treatment_period, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
by = "mz_rt")
head(tom_effect_list, n=1)## # A tibble: 1 × 19
## mz_rt mz rt Cluster_ID Cluster_features Cluster_size mean_control_FC
## <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 244.908_… 245. 0.706 Cluster_2… 244.908_0.706;2… 2 1.07
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## # mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## # MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## # control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>
# make comparison list for comparison tests
my_comparisons <- list( c("beta_b1", "beta_b3"),
c("red_b1", "red_b3"),
c("control_b1", "control_b3") )# subset df for uniquely significant features
justT_tom_effect_df <- df_for_stats_wide %>%
dplyr::select(c(1:21),
(all_of(overall_tomato_effect$mz_rt)))
# make tidy df
justT_tom_effect_df_tidy <- justT_tom_effect_df %>%
pivot_longer(cols = 22:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# fix factor levels for time points
justT_tom_effect_df_tidy$treatment_period <- factor(justT_tom_effect_df_tidy$treatment_period,
levels = c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3"))
# check
levels(justT_tom_effect_df_tidy$treatment_period) ## [1] "control_b1" "control_b3" "beta_b1" "beta_b3" "red_b1"
## [6] "red_b3"
justT_tom_effect_df_tidy$treatment <- factor(justT_tom_effect_df_tidy$treatment,
levels = c("control", "beta", "red"))justT_tom_effect_df_tidy %>%
ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
geom_boxplot() +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_x_discrete(labels = c("", "", "", "", "", "")) +
geom_line(aes(group = subject, colour = subject), size = 0.2) +
theme_classic(base_size = 12, base_family = "sans") +
facet_wrap(vars(mz_rt), scales = "free_y", ncol = 4) +
stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
labs(x = "",
y = "Log2 transformed relative abundance",
title = "Tomato effect - significant when compared to control, also significant pre and post tomato",
subtitle = "FDR adj. p-values from T-tests")## [1] 167 20
Which features are significant in paired t test comparisons for beta, but not significant in the same direction for control?
Features that are significant in both pre- vs. post. beta AND post-beta vs. post-control
# select features that are only significant post beta v. post control comparison
overall_beta_effect <- semi_join(sig_paired_beta_rmBG,
sig_beta_v_ctrl_t.test,
by = "mz_rt")
dim(overall_beta_effect)## [1] 5 20
overall_unique_beta_effect <- left_join(overall_beta_effect,
sig_beta_v_red_t.test,
by = "mz_rt")
dim(overall_unique_beta_effect)## [1] 5 29
(FC_beta_effect <- df_for_stats %>%
filter(mz_rt %in% overall_beta_effect$mz_rt) %>%
select(subject, treatment_period, mz_rt, rel_abund) %>%
group_by(mz_rt) %>%
pivot_wider(names_from = treatment_period,
values_from = rel_abund) %>%
mutate(control_FC = control_b3/control_b1,
beta_FC = beta_b3/beta_b1,
red_FC = red_b3/red_b1) %>%
summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
mean_beta_FC = mean(beta_FC, na.rm = TRUE),
mean_red_FC = mean(red_FC, na.rm = TRUE),
mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))## # A tibble: 5 × 7
## mz_rt mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 847.6455_10.298 0.913 0.857 0.927 0.938
## 2 896.6737_10.623 0.997 0.861 0.936 0.863
## 3 917.633_10.292 1.01 0.891 0.980 0.886
## 4 945.6385_9.975 1.02 0.853 0.924 0.836
## 5 967.6322_9.976 0.980 0.873 0.903 0.891
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>
# combine cluster and FC info
beta_effect_list <- left_join(FC_beta_effect,
cluster_features,
by = "mz_rt") %>%
select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())# add in averages for each group + timepoint
beta_effect_list <-
left_join(beta_effect_list,
(df_for_stats %>%
group_by(treatment_period, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
by = "mz_rt")
head(beta_effect_list, n=1)## # A tibble: 1 × 19
## mz_rt mz rt Cluster_ID Cluster_features Cluster_size mean_control_FC
## <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 847.6455… 848. 10.3 847.6455_… 847.6455_10.298 1 0.913
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## # mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## # MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## # control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>
# subset df for uniquely significant features
justT_beta_effect_df <- df_for_stats_wide %>%
dplyr::select(c(1:21),
(all_of(overall_beta_effect$mz_rt)))
# make tidy df
justT_beta_effect_df_tidy <- justT_beta_effect_df %>%
pivot_longer(cols = 22:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# fix factor levels for time points
justT_beta_effect_df_tidy$treatment_period <- factor(justT_beta_effect_df_tidy$treatment_period,
levels = c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3"))
# check
levels(justT_beta_effect_df_tidy$treatment_period) ## [1] "control_b1" "control_b3" "beta_b1" "beta_b3" "red_b1"
## [6] "red_b3"
justT_beta_effect_df_tidy$treatment <- factor(justT_beta_effect_df_tidy$treatment,
levels = c("control", "beta", "red"))justT_beta_effect_df_tidy %>%
ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
geom_boxplot() +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_x_discrete(labels = c("", "", "", "", "", "")) +
geom_line(aes(group = subject, colour = subject), size = 0.2) +
theme_classic(base_size = 12, base_family = "sans") +
facet_wrap(vars(mz_rt), scales = "free_y", nrow = 3) +
stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
labs(x = "",
y = "Log2 transformed relative abundance",
title = "High beta juice effect - significant when compared to control, also significant pre and post beta juice",
subtitle = "FDR adj. p-values from T-tests")## [1] 95 20
# select features that are only significant post red v. post control comparison
overall_red_effect <- semi_join(sig_paired_red_rmBG,
sig_red_v_ctrl_t.test,
by = "mz_rt")
dim(overall_red_effect)## [1] 1 20
# select features that are only significant post tomato v. post control comparison
overall_unique_red_effect <- left_join(overall_red_effect,
sig_beta_v_red_t.test,
by = "mz_rt")
dim(overall_unique_red_effect)## [1] 1 29
## # A tibble: 1 × 29
## mz_rt .y..red group1.red group2.red n1.red n2.red statistic.red df.red p.red
## <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 945.6… rel_ab… b1 b3 12 12 2.33 11 0.0396
## # ℹ 20 more variables: p.signif.red <chr>, .y..ctrl <chr>, group1.ctrl <chr>,
## # group2.ctrl <chr>, n1.ctrl <int>, n2.ctrl <int>, statistic.ctrl <dbl>,
## # df.ctrl <dbl>, p.ctrl <dbl>, p.signif.ctrl <chr>, sign <dbl>, .y. <chr>,
## # group1 <chr>, group2 <chr>, n1 <int>, n2 <int>, statistic <dbl>, df <dbl>,
## # p <dbl>, p.signif <chr>
(FC_red_effect <- df_for_stats %>%
filter(mz_rt %in% overall_red_effect$mz_rt) %>%
select(subject, treatment_period, mz_rt, rel_abund) %>%
group_by(mz_rt) %>%
pivot_wider(names_from = treatment_period,
values_from = rel_abund) %>%
mutate(control_FC = control_b3/control_b1,
beta_FC = beta_b3/beta_b1,
red_FC = red_b3/red_b1) %>%
summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
mean_beta_FC = mean(beta_FC, na.rm = TRUE),
mean_red_FC = mean(red_FC, na.rm = TRUE),
mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))## # A tibble: 1 × 7
## mz_rt mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 945.6385_9.975 1.02 0.853 0.924 0.836
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>
# combine cluster and FC info
red_effect_list <- left_join(FC_red_effect,
cluster_features,
by = "mz_rt") %>%
select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())# add in averages for each group + timepoint
red_effect_list <-
left_join(red_effect_list,
(df_for_stats %>%
group_by(treatment_period, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
by = "mz_rt")
head(red_effect_list, n=1)## # A tibble: 1 × 19
## mz_rt mz rt Cluster_ID Cluster_features Cluster_size mean_control_FC
## <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 945.6385… 946. 9.98 945.6385_… 945.6385_9.975 1 1.02
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## # mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## # MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## # control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>
# subset df for uniquely significant features
justT_red_effect_df <- df_for_stats_wide %>%
dplyr::select(c(1:21),
(all_of(overall_unique_red_effect$mz_rt)))
# make tidy df
justT_red_effect_df_tidy <- justT_red_effect_df %>%
pivot_longer(cols = 22:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# fix factor levels for time points
justT_red_effect_df_tidy$treatment_period <- factor(justT_red_effect_df_tidy$treatment_period,
levels = c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3"))
# check
levels(justT_red_effect_df_tidy$treatment_period) ## [1] "control_b1" "control_b3" "beta_b1" "beta_b3" "red_b1"
## [6] "red_b3"
justT_red_effect_df_tidy$treatment <- factor(justT_red_effect_df_tidy$treatment,
levels = c("control", "beta", "red"))justT_red_effect_df_tidy %>%
ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
geom_boxplot() +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_x_discrete(labels = c("", "", "", "", "", "")) +
geom_line(aes(group = subject, colour = subject), size = 0.2) +
theme_classic(base_size = 12, base_family = "sans") +
facet_wrap(vars(mz_rt), scales = "free_y") +
stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
labs(x = "",
y = "Log2 transformed relative abundance",
title = "High lyc effect - significant when compared to control, \nsignificant pre vs post red",
subtitle = "FDR adj. p-values from T-tests")